Respirator fitting device and method

ABSTRACT

A system and method for automated respirator fit testing by comparing three-dimensional (3D) images are disclosed. An example embodiment is configured to: obtain at least one three-dimensional facial image of an individual at an initial visit (Visit X); capture at least one current 3D facial image of the individual at a subsequent visit (Visit X+n); convert the Visit X image and the Visit X+n image to numerical data for computation and analysis; identify reference points in the Visit X data and the Visit X+n data; determine if the Visit X data and the Visit X+n data is sufficiently aligned; determine if any differences between the VISIT X data and the VISIT X+n data are greater than a pre-defined set of Allowable Deltas (ADs); and record a pass status if the differences between the VISIT X data and the VISIT X+n data are not greater than the pre-defined ADs.

RELATED PATENT APPLICATIONS

This patent application claims the benefit of U.S. Provisional PatentApplication Nos. 62/691,485 filed on Jun. 28, 2018, entitled SYSTEM ANDMETHOD FOR AUTOMATED RESPIRATOR FIT TESTING BY COMPARINGTHREE-DIMENSIONAL (3D) IMAGES, 62/733,290 filed on Sep. 19, 2018,entitled RESPIRATOR FITTING DEVICE AND METHOD, and 62/782,684 filed onDec. 20, 2018, entitled RESPIRATOR FITTING DEVICE AND METHOD, which areexpressly incorporated herein by reference in their entireties.

COPYRIGHT

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction of the patent document or thepatent disclosure, as it appears in the Patent and Trademark Officepatent files or records, but otherwise reserves all copyright rightswhatsoever. The following notice applies to the software and data asdescribed below and in the drawings that form a part of this document:Copyright 2018 Michael GUGINO, All Rights Reserved.

TECHNICAL FIELD

This patent application relates to computer-implemented softwaresystems, image processing, image manipulation, and automated respiratorfit testing according to one embodiment, and more specifically to asystem and method for automated respirator fit testing by comparingthree-dimensional (3D) images.

BACKGROUND

A respirator is a piece of personal protective equipment worn on theface which covers at least the nose and mouth, and is used to reduce theuser's risk of inhaling hazardous airborne particles (including dustparticles and infectious agents), gases or vapors. Types of respiratorsinclude particulate respirators, which filter out airborne particles;“gas masks,” which filter out chemicals and gases; airline respirators,which use compressed air from a remote source; and self-containedbreathing apparatus, which include their own air supply. Employers aremandated toLIEN ensure employees wear properly fitted respirators whentheir use can abate hazards related to atmospheric conditions.

Problems with conventional methods of fit testing a respirator mask toan employee (for example, and/or other respirator users) include thecumbersome nature of the fit test itself, the extensive time it takes toperform the fit test, and a test's susceptibility to seemingly innocuousenvironmental conditions. Also, when an respirator user is required towear more than one respirator for their job, a separate fit test willlikely be performed for each respirator. In U.S. Pat. No. 10,061,888 B2(granted on Aug. 28, 2018), the applicant identified similar problemswith the current respirator fit technology citing the same general lackof efficiency and practicality. The applicant identified a need for anew and improved system for predicting an optimal fit of a respirator toa facial area.

A Quantitative Fit Test (QNFT) measures the fit factor between therespirator mask and a respirator user (RU). This fit test can be as longas 15-20 minutes. The fit factor is the ratio of the airborne test agentconcentration outside the respirator mask to the test agentconcentration inside the respirator. It may also be the ratio of totalairflow through the respirator (e.g., modeled by the fit testinstrument) to the airflow through respirator mask face-seal leaks. QNFTmachines are necessarily highly sensitive pieces of machinery. Seeminglyinsignificant environmental variations or test subject factors caneasily derail the fit test process. For example, any excess (compared torequired test standards) amount of airborne particulate can invalidatefit tests. On the other hand, because modern HVAC systems run soeffectively with HEPA filtration systems, ambient air can often lack theminimum airborne particulate requirements, which may also invalidate fittests. In other situations, a test subject may simply be extremelyfidgety or claustrophobic, which can cause invalid test results.

A Qualitative Fit Test (QLFT) is a pass/fail test that relies on theindividual's sensory detection of a test agent, such as taste, smell, orinvoluntary cough (a reaction to irritant smoke). Heavy smokers may notreact properly to sensory irritants. Depending on the type of QLFT beingperformed, these tests can be extremely long in duration (15-20 minutes)or the test subject can easily provide false results. Test subjectsfrequently provide false results because their employment status candepend on the results.

Because of these and other factors, unsuccessful or invalid fit testresults are common. An unsuccessful or invalid fit test requiresretesting, which can stretch a typical 15-20 minute test into 45 minutesor more. A workplace with hundreds of RU's will often experience fittesting backlogs causing frustration and increased costs. Theconventional fit test process presents a substantial logisticalchallenge in a workplace with a large population of RU's. Moreover, mostcountries require RU's, as employees, to be paid while taking part infit tests, which will often times trigger overtime costs for theemployer.

A typical large employer (e.g., a hospital with 2000 RUs) often hires athird-party vendor to spend a week and $120,000, in an attempt to fittest employees who use respirators.

As described above, an RU is fit tested periodically to determine fits(against an RU's face) for respirator masks from specific manufacturers,with specific models, and sizes. In the United States, for example, afit test is mandated by Occupational Safety and Health Administration(OSHA) regulation 29 CFR Part 1910.134, annually.

The annual fit testing requirement was adopted by OSHA in 1998. A 2001survey of 269,389 businesses requiring employees to wear respiratorsfound that only 57% of these performed fit testing. Federal OSHA reportsthat, over the last ten years, respiratory protection program violationsare consistently in the top five citations issued to employers and fittesting is the third most common factor in employers' non-compliancestatus. Moreover, a majority of the employers in European Unioncountries required to adopt fit testing procedures are not compliant.The main reason for non-compliance appears to be the cumbersome natureof the fit test itself.

A notably substantial percentage of RU's are successfully fitted to thesame respirator manufacturer, model, and size from a prior visit. Infact, during the public comment period for OSHA's rule-making, data fromprivate companies were considered in establishing the annual fit testrequirement. The Texas Chemical Council indicated that, “virtually noindividuals fail fit tests a year after initial testing.” The ExxonCompany reported a less than 1% annual fit test failure rate. Moreover,NIOSH (National Institute of Occupational Safety and Health) hasindicated that if an RU hasn't had a significant change in weight (morethan 20 pounds), the chances of such a successful subsequent fit testcan range from 74.6 percent to 89.6 percent over a three-year period.

Given all the challenges of conventional fit test technology, it is notuncommon for regulatory compliance in a workplace to run well under 50percent. Even employers who self-report themselves as compliant oftenwill shortcut or skip a number of steps because the process is socumbersome and time consuming. Current methods for automated respiratorfit testing are ineffective and inefficient in protecting manyrespirator users, but no substantial technological improvements haveoccurred in this field. Currently there is no viable method to speed upthe respirator fit test process using 3D image technology.

SUMMARY

In various example embodiments as disclosed herein, an apparatus andassociated method is described, which may relate to a system forpredicting a respiratory protection device fit by comparingvisit-over-visit data obtained from 2D and/or 3D images, weightinformation, age information, body mass index (BMI) information, and/orother information. Visit-over-visit deviations may be compared to apredetermined allowable delta (AD) thresholds (e.g., as describedherein) to determine a successful or unsuccessful fit of a respiratoryprotection device (e.g., a respirator mask). This new method isconfigured to reduce fit test processing time from 15 to 20 minutes downto about 2.5 minutes or less, for example. The images and associateddata (e.g., data determined based on the images, weight data, age data,BMI data, and/or other data) being compared include the baseline imageof the face and head (and/or measurements determined therefrom) of anindividual RU at the time of a successful conventional respiratoryprotection device fit test (VISIT X), face and head measurements fromsubsequent images (VISIT X+n) at intervals mandated by safetyregulations, intervals determined based on AD values determined fromlaboratory studies and analysis, and/or other intervals. The data fromthe VISIT X image is compared to data taken from subsequent imagescaptured in the future (VISIT X+n) and the AD's. Weight data, age data,BMI data, and/or other data may (these examples are not intended to belimiting) also be compared visit over visit and compared tocorresponding AD's.

In various example embodiments, the data being analyzed may include U.S.Federal and/or state or any other non-U.S. regulatoryauthority-identified criteria, which may include 3D facial and headtopography data (e.g., linear, surface area, and volumetric data), the3D image itself, 2D image measurements, a person's weight, age, bodymass index (BMI), medical history, history of surgeries and/or facialscars, facial dimensions, and any other information deemed appropriate.Visit-over-visit deviations may be compared to a predetermined thresholdof allowable deltas (AD) to determine a successful or unsuccessful fitof the respiratory protection device. In various example embodiments,the data can also be extrapolated to determine the most likely date ofexpected failure of the respirator fit. The various example embodimentsare described in more detail below.

BRIEF DESCRIPTION OF THE DRAWINGS

The various embodiments are illustrated by way of example, and not byway of limitation, in the figures of the accompanying drawings in which:

FIGS. 1 and 2 illustrate the traditional processes for performingconventional respirator fit testing;

FIGS. 3 through 5 illustrate a process for capturing a 3D image or setof images of an individual's face and head for analysis by an exampleembodiment;

FIGS. 6 and 7 illustrate a sample of at least a portion of the resulting3D images captured for analysis by an example embodiment;

FIG. 8 illustrates a process flow diagram that shows an exampleembodiment of a method as described herein;

FIG. 9 illustrates another process flow diagram that shows an exampleembodiment of a method as described herein; and

FIG. 10 shows a diagrammatic representation of a machine in the exampleform of a computer system within which a set of instructions whenexecuted may cause the machine to perform any one or more of themethodologies discussed herein.

FIG. 11 illustrates another view of a processor and logic of the systemshown in FIG. 10 which, when executed, may cause the system to performany one or more of the methodologies discussed herein.

FIG. 12 illustrates example virtual cube external section volumesmeasured and/or used to determine allowable deltas (ADs) as describedherein.

FIG. 13 illustrates example surface areas measured and/or used todetermine allowable deltas as described herein.

FIG. 14 illustrates example point-to-point distances measured and/orused to determine allowable deltas as described herein.

DETAILED DESCRIPTION

In the following description, for purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the various embodiments. It will be evident, however,to one of ordinary skill in the art that the various embodiments may bepracticed without these specific details.

To mitigate the problems described herein, the inventors had to bothinvent solutions and, in some cases just as importantly, recognizeproblems overlooked (or not yet foreseen) by others in the field ofrespirator mask fitting. Indeed, the inventors wish to emphasize thedifficulty of recognizing those problems that are nascent and willbecome much more apparent in the future should trends in industrycontinue as the inventors expect. Further, because multiple problems areaddressed, it should be understood that some embodiments areproblem-specific, and not all embodiments address every problem withtraditional systems described herein or provide every benefit describedherein. That said, improvements that solve various permutations of theseproblems are described below.

In various example embodiments described herein, a system and method forautomated respirator fit testing by comparing two and/orthree-dimensional (2D and/or 3D) images are disclosed. In the variousexample embodiments described herein, a computer-implemented tool orsoftware application (app) as part of a respirator fit test processingsystem is described to automate and improve the collection and analysisof respirator fit data of an individual being tested. A computer orcomputing system on which the described embodiments can be implementedcan include personal computers (PCs), portable computing devices,laptops, tablet computers, personal digital assistants (PDAs), personalcommunication devices (e.g., cellular telephones, smartphones, or otherwireless devices), network computers, consumer electronic devices, orany other type of computing, data processing, communication, networking,or electronic system.

FIGS. 1 and 2 illustrate the traditional processes for performingconventional respirator fit testing. Referring to FIGS. 1 and 2, atVISIT X, when a person is successfully fit tested for a respirator (seeFIG. 1 or FIG. 2), identifying personal information and associatedrespirator information (e.g., manufacturer, model and size) are loggedinto a database.

In the various example embodiments described herein, a 3D image or setof (3D and/or 2D) images of the individual's face and head may also becaptured (see FIGS. 3 through 5) at an initial respirator mask fittingvisit. As shown in FIG. 3, a camera can be positioned below and to theside of the individual's face to capture an image of the individual'sface or head. In an example embodiment, a VECTRA™ H1 handheld imagingsystem or similar system can be used to capture the 3D images. As shownin FIG. 4, the camera can be positioned in front of the individual'sface to capture a frontal image of the individual's face or head. Asshown in FIG. 5, the camera can be positioned below and to the alternateside of the individual's face to capture another image of theindividual's face or head. In an example embodiment, a sample of atleast a portion of the resulting 3D images is shown in FIGS. 6 and 7. Asshown in FIG. 7, particular points or locations on the face or head ofthe individual RU in the image set can be identified and saved asreference points to compare images of the RU between a VISIT X and aVISIT X+n. Reference points can be chosen from universal landmarks whichare unlikely to change (e.g. eye sockets) and/or topographical landmarkswith the least amount of tissue between the bone and skin (e.g. bridgeof the nose). The RU's individual data file can be populated with the 3Dimage or set of images of the individual's face and head and thereference points for analysis by an example embodiment. The 3D imagedata (for example: data points, reference points, linear and surfacearea topography, 2D data and volumetric data, etc.) can be converted tonumerical values for mathematical computation and analysis.

At VISIT X+n (e.g., a subsequent respirator mask fitting visit), acurrent 3D image or set of images of the individual's face and head isagain captured. In an example embodiment, a VECTRA™ H1 handheld imagingsystem or similar system can also be used to capture the 3D images. Inother embodiments, images can be captured off-site using an app on apersonal device (e.g., mobile phone) and the captured images can besubmitted by the RU electronically. The data from the current 3D imageor set of images of the individual's face and head can be converted tonumerical values. The VISIT X+n numerical data is then compared to theimage data from VISIT X to determine if common reference points (e.g.,the forehead, upper portion of the nose and temples, see FIG. 7) betweenVISIT X and VISIT X+n are properly aligned to produce valid comparisonresults. If the common reference points are properly aligned, the VISITX+n numerical data is compared to VISIT X data to determine if anydeviations or a conglomerate of those deviations are withinpre-determined allowable deltas (ADs) and/or other threshold values.

If the mathematical deviations between the VISIT X 3D facial image dataand VISIT X+n 3D facial image data are equal to or less than the ADs,the RU is considered to have a successful fit test for the samemanufacturer, model, and size respirator identified in VISIT X for anadditional period of time (e.g., 12 months in the United States; longerperiod of time in other countries). Based on a computed rate at whichthe 3D facial image data is approaching the Allowable Deltas, anExpected failure date can be computed. If the mathematical deviationsbetween the VISIT X 3D facial image data and VISIT X+n 3D facial imagedata are greater than the ADs, the RU is considered to have anunsuccessful fit test and must participate in a conventional QNFT orQLFT.

In some embodiments, the method described herein includes generating amask fit pass indication responsive to differences between thecorresponding facial features, facial dimensions, and/or faciallocations on the face of the individual represented in initial visitdata and subsequent visit data not breaching the one or more pre-definedADs. In some embodiments, the method includes generating a mask fit failindication responsive to differences between the corresponding facialfeatures, facial dimensions, and/or facial locations on the face of theindividual represented in the initial visit data and subsequent visitdata breaching the one or more pre-defined ADs. Advantageously, thepresent method of generating the pass or fail indications may beperformed for two or more different types (e.g., differentmanufacturers, models, and/or sizes) of respirator of respirator masksusing the same initial visit data and subsequent visit data. Forexample, a law enforcement officers may need to be fit tested for both afull-face chemical (e.g., tear gas) respirator mask, and/or otherpartial-face respirator masks (e.g. N95 healthcare respirator masks)that may be used in the line of duty. In some embodiments, the AD's maybe representative of, and/or determined based at least in part on, amanufacturer's databases of mask dimensions, etc. (accessed as describedherein). In some embodiments, the AD's may change depending on whichmask(s) an individual is required to be fit tested on.

In some embodiments, ADs may be determined based on either a variance orinterim order granted by Federal and/or state OSHA's or any otherappropriate regulatory authority. In some embodiments (e.g., asdescribed below), AD's may be determined based on data gathered for apopulation of users and/or other information. ADs may be adopted intoregulations or they may be entered by an amendment into the regulations.Currently, the International Organization for Standardization, which isvery “wearer-centric focused” does not require periodic fit tests beyondthe initial fit test. The addition of a periodic respirator fit test asperformed by the example embodiments described herein would provide asubstantially higher degree of protection for the RU while still being“wearer-centric focused.” If the ISO and the United States both adoptedthe use of a periodic respirator fit test, the global standardizationwould take a tremendous step forward and greatly assist multi-nationalcompanies in protecting their employees and RU's.

In some embodiments, the periodic respirator fit test as performed bythe example embodiments described herein may be mandated at shorter timeintervals or either before each use of a respirator or at the beginningof an RU's shift, when VISIT X and VISIT X+1 data can be more quicklycompared to ADs or completely automated.

FIG. 8 illustrates a process flow diagram that shows an exampleembodiment of a method as described herein. Referring now to FIG. 8, atVISIT X: Respirator User (RU) is successfully fit tested for a specificmodel, manufacturer and size of respirator, using a conventionalmethodology (see FIG. 1 or FIG. 2). RU identifying information,respirator manufacturer, model and size is entered into an individual RUdata file (Process Block 110). A 3D image or set of facial images of theRU is captured (see FIGS. 3 through 5), saved in the RU data file, andthe saved data is converted to numerical values for subsequentcomputation and analysis (Process Block 115). At VISIT X+n: The saveddata may be compared to a predetermined threshold of allowable deltas(AD). Has the RU reported weight changes greater than ADs, dental orcosmetic surgery, facial scarring or is facial scarring visible sinceVISIT X? (Process Block 120). If yes, a new conventional fit test isrequired at process block 110. A current 3D image or current set of 3Dfacial images of the RU can be captured (see FIGS. 3 through 5), savedin the RU data file, and the current data is converted to numericalvalues or data for computation and analysis (Process Block 125). Are the3D facial image data points from VISIT X aligned well enough with the 3Dfacial image data points from VISIT X+n (see FIGS. 6 and 7) to producevalid analytical results? (Process Block 130). If not, process block 125is repeated and a new current 3D image or current set of 3D facialimages of the RU can be captured. The 3D facial image data points fromVISIT X+n are compared to the 3D facial image data points from VISIT X(Process Block 135). Are the differences between the VISIT X+n 3D facialimage data and VISIT X 3D facial image data greater than the AllowableDeltas? (Process Block 140) If yes, a new conventional fit test isrequired at process block 110. (Process Block 155). If the differencesbetween the VISIT X+n 3D facial image data and VISIT X 3D facial imagedata is not greater than the Allowable Deltas, a PASS status is recordedand the respirator fit test is successful. Identifying information,respirator manufacturer, model, and size of respirator is saved in theRU data file. (Process Block 145). Based on a computed rate at which the3D facial image data is approaching the Allowable Deltas, an Expectedfailure date can be computed. (Process Block 150).

FIG. 9 illustrates another process flow diagram that shows an exampleembodiment of a method as described herein. The method 2000 of anexample embodiment is configured to: obtain at least onethree-dimensional (3D) facial image of an individual at an initial visit(Visit X) (processing block 2010); capture at least one current 3Dfacial image of the individual at a subsequent visit (Visit X+n)(processing block 2020); convert the Visit X image and the Visit X+nimage to numerical data for computation and analysis (processing block2030); identify reference points in the Visit X data and the Visit X+ndata (processing block 2040); determine if the Visit X data and theVisit X+n data is sufficiently aligned (processing block 2050);determine if any differences between the VISIT X data and the VISIT X+ndata are greater than a pre-defined set of Allowable Deltas (ADs)(processing block 2060); and record a pass status if the differencesbetween the VISIT X data and the VISIT X+n data are not greater than thepre-defined ADs (processing block 2070).

FIG. 10 shows a diagrammatic representation of a machine in the exampleform of a mobile computing and/or communication system 700 within whicha set of instructions when executed and/or processing logic whenactivated may cause the machine to perform any one or more of themethodologies described and/or claimed herein. In alternativeembodiments, the machine operates as a standalone device or may beconnected (e.g., networked) to other machines. In a networkeddeployment, the machine may operate in the capacity of a server or aclient machine in server-client network environment, or as a peermachine in a peer-to-peer (or distributed) network environment. Themachine may be a personal computer (PC), a laptop computer, a tabletcomputing system, a Personal Digital Assistant (PDA), a cellulartelephone, a smartphone, a mobile device, a web appliance, a networkrouter, switch or bridge, or any machine capable of executing a set ofinstructions (sequential or otherwise) or activating processing logicthat specify actions to be taken by that machine. Further, while only asingle machine is illustrated, the term “machine” can also be taken toinclude any collection of machines that individually or jointly executea set (or multiple sets) of instructions or processing logic to performany one or more of the methodologies described and/or claimed herein.

The example mobile computing and/or communication system 700 includesone or more data processors 702 (e.g., a System-on-a-Chip (SoC), generalprocessing core, graphics core, and optionally other processing logic)and a memory 704, which can communicate with each other via a bus orother data transfer system 706. The mobile computing and/orcommunication system 700 may further include various input/output (I/O)devices and/or interfaces 710, such as a touchscreen display andoptionally a network interface 712. In an example embodiment, thenetwork interface 712 can include one or more radio transceiversconfigured for compatibility with any one or more standard wirelessand/or cellular protocols or access technologies (e.g., 2nd (2G), 2.5,3rd (3G), 4th (4G) generation, and future generation radio access forcellular systems, Global System for Mobile communication (GSM), GeneralPacket Radio Services (GPRS), Enhanced Data GSM Environment (EDGE),Wideband Code Division Multiple Access (WCDMA), LTE, CDMA2000, WLAN,Wireless Router (WR) mesh, and the like). Network interface 712 may alsobe configured for use with various other wired and/or wirelesscommunication protocols, including TCP/IP, UDP, SIP, SMS, RTP, WAP,CDMA, TDMA, UMTS, UWB, WiFi, WiMax, Bluetooth™, IEEE 802.11x, and thelike. In essence, network interface 712 may include or support virtuallyany wired and/or wireless communication mechanisms by which informationmay travel between the mobile computing and/or communication system 700and another computing or communication system via network 714.

The memory 704 can represent a machine-readable medium on which isstored one or more sets of instructions, software, firmware, or otherprocessing logic (e.g., logic 708) embodying any one or more of themethodologies or functions described and/or claimed herein. The logic708, or a portion thereof, may also reside, completely or at leastpartially within the processor 702 during execution thereof by themobile computing and/or communication system 700. As such, the memory704 and the processor 702 may also constitute machine-readable media.The logic 708, or a portion thereof, may also be configured asprocessing logic or logic, at least a portion of which is partiallyimplemented in hardware. The logic 708, or a portion thereof, mayfurther be transmitted or received over a network 714 via the networkinterface 712. While the machine-readable medium of an exampleembodiment can be a single medium, the term “machine-readable medium”should be taken to include a single non-transitory medium or multiplenon-transitory media (e.g., a centralized or distributed database,and/or associated caches and computing systems) that stores the one ormore sets of instructions. The term “machine-readable medium” can alsobe taken to include any non-transitory medium that is capable ofstoring, encoding or carrying a set of instructions for execution by themachine and that cause the machine to perform any one or more of themethodologies of the various embodiments, or that is capable of storing,encoding or carrying data structures utilized by or associated with sucha set of instructions. The term “machine-readable medium” canaccordingly be taken to include, but not be limited to, solid-statememories, optical media, and magnetic media.

Embodiments of the techniques described herein may be implemented usinga single instance of computing and/or communication system 700 ormultiple systems 700 configured to host different portions or instancesof embodiments. Multiple systems 700 may provide for parallel orsequential processing/execution of one or more portions of thetechniques described herein.

Those skilled in the art will also appreciate that while various itemsare illustrated as being stored in memory or on storage while beingused, these items or portions of them may be transferred between memoryand other storage devices for purposes of memory management and dataintegrity. Alternatively, in other embodiments some or all of thesoftware components may execute in memory on another device andcommunicate with the illustrated computer system via inter-computercommunication. Some or all of the system components or data structuresmay also be stored (e.g., as instructions or structured data) on acomputer-accessible medium or a portable article to be read by anappropriate drive, various examples of which are described above. Insome embodiments, instructions stored on a computer-accessible mediumseparate from system 700 may be transmitted to computer system 700 viatransmission media or signals such as electrical, electromagnetic, ordigital signals, conveyed via a communication medium such as a networkor a wireless link. Various embodiments may further include receiving,sending, or storing instructions or data implemented in accordance withthe foregoing description upon a computer-accessible medium.Accordingly, the present invention may be practiced with other computersystem configurations.

As described herein for various example embodiments, a system and methodfor automated respirator fit testing by comparing two and/orthree-dimensional (2D and/or 3D) images are disclosed. In the variousexample embodiments described herein, a computer-implemented tool orsoftware application (app) as part of a respirator fit test processingsystem is described to automate and improve the collection and analysisof 2D and/or 3D facial image data for respirator fit testing. In anexample embodiment, 3D facial image data is automatically analyzed usingdata processing and image processing techniques to provide real-timefeedback to the individual and testing facility. In various exampleembodiments described herein, the respirator fit test processing systemprovides an automated respirator fit testing system as it relates to theindustries that use respirators, specifically, to government andcommercial entities. As such, the various embodiments as describedherein are necessarily rooted in computer processing, image processing,and network technology and serve to improve these technologies whenapplied in the manner as presently claimed. In particular, the variousembodiments described herein improve the use of data processing systems,3D image processing systems, mobile device technology, and data networktechnology in the context of automated respirator fit testing viaelectronic means.

FIG. 11 illustrates another view of processor 702 and logic 708 ofsystem 700 shown in FIG. 10 which, when executed, may cause the systemto perform any one or more of the methodologies discussed herein.

As described above, processor 702 is configured to provide informationprocessing capabilities in system 700. As such, processor 702 maycomprise one or more of a digital processor, an analog processor, adigital circuit designed to process information, an analog circuitdesigned to process information, a state machine, and/or othermechanisms for electronically processing information. Although processor702 is shown in FIG. 11 (and FIG. 10) as a single entity, this is forillustrative purposes only. In some embodiments, processor 702 maycomprise a plurality of processing units. These processing units may bephysically located within the same device (e.g., system 700), orprocessor 702 may represent processing functionality of a plurality ofdevices operating in coordination. In some embodiments, processor 702may be and/or be included in a computing device 700 such as a desktopcomputer, a laptop computer, a smartphone, a tablet computer, a server,and/or other computing devices as described above. Such computingdevices may run one or more electronic applications having graphicaluser interfaces configured to facilitate user interaction with system700.

As shown in FIG. 11, processor 702 is configured to execute one or morecomputer program components. The computer program components maycomprise software programs and/or algorithms coded and/or otherwiseembedded in processor 702, for example. The computer program componentsmay comprise one or more of a user information component 800, an imagecomponent 802, a conversion component 804, an alignment component 806, afitting component 808, a prediction component 810, and/or othercomponents. Processor 702 may be configured to execute components 800,802, 804, 806, 808, and/or 810 by software; hardware; firmware; somecombination of software, hardware, and/or firmware; and/or othermechanisms for configuring processing capabilities on processor 702.

It should be appreciated that although components 800, 802, 804, 806,808, and 810 are illustrated in FIG. 11 as being co-located within asingle processing unit, in embodiments in which processor 702 comprisesmultiple processing units, one or more of components 800, 802, 804, 806,808, and/or 810 may be located remotely from the other components. Thedescription of the functionality provided by the different components800, 802, 804, 806, 808, and/or 810 described herein is for illustrativepurposes, and is not intended to be limiting, as any of components 800,802, 804, 806, 808, and/or 810 may provide more or less functionalitythan is described. For example, one or more of components 800, 802, 804,806, 808, and/or 810 may be eliminated, and some or all of itsfunctionality may be provided by other components 800, 802, 804, 806,808, and/or 810. As another example, processor 702 may be configured toexecute one or more additional components that may perform some or allof the functionality attributed below to one of components 800, 802,804, 806, 808, and/or 810.

User information component 800 is configured to obtain physical,demographic, and/or other information about an individual being fittedfor a respirator mask. For example, user information component 800 maybe configured to obtain weight information for an individual at aninitial respirator mask fitting visit (e.g., a VISIT X as describedabove) and subsequent respirator mask fitting visits (e.g., a VISIT X+nas described above). As another example, user information component 800may be configured to obtain information related to facial scarringand/or other facial shape changes that have occurred since a prior maskfitting visit. As another example, user information component 800 may beconfigured to obtain demographic information for the individual at theinitial respirator mask fitting visit and/or the subsequent respiratormask fitting visit. The demographic information may comprisegeographical information about a location of the individual, racialinformation about the individual, information about an age and/or genderof the individual, health information about the individual, informationabout an industry where the individual works, public health informationrelated to the industry where the individual works, and/or otherdemographic information.

In some embodiments, user information component 800 is configured toobtain information from entries and/or selections made by via a userinterface of the present system. In some embodiments, user informationcomponent 800 is configured to obtain information electronically fromexternal resources (e.g., a medical records storage system of a healthcare provider), electronic storage (e.g., memory 704 shown in FIG. 10)included in system 700, and/or other sources of information. In someembodiments, electronically obtaining information comprises querying onemore databases and/or servers; uploading information and/or downloadinginformation, facilitating user input (e.g., via I/O device 710 shown inFIG. 10), sending and/or receiving emails, sending and/or receiving textmessages, and/or sending and/or receiving other communications, and/orother obtaining operations. In some embodiments, user informationcomponent 800 is configured to aggregate information from varioussources (e.g., one or more of the external resources described above,electronic storage, etc.), arrange the information in one or moreelectronic databases (e.g., electronic storage, and/or other electronicdatabases), and/or perform other operations.

Image component 802 is configured to obtain at least one initial 2Dand/or 3D facial image of an individual from an initial respirator maskfitting visit (e.g., VISIT X), at least one current 2D and/or 3D facialimage of the individual from a subsequent respirator mask fitting visit(e.g., a VISIT X+n), and/or other image information. The facial images(e.g., at least one initial 3D image and at least one current 3D image)of the individual may be the 3D image or set of images of theindividual's face and/or head captured as described above and shown inFIG. 3-7 (e.g., at different mask fitting visits and/or at other times),for example. The 3D facial images (at least one initial 3D image and atleast one current 3D image) of the individual may be and/or include the3D image data (for example: data points, reference points, linear andsurface area topography, 2D data and volumetric data, etc.) describedabove.

Conversion component 804 is configured to convert the at least oneinitial facial image and the at least one current facial image tonumerical initial visit data and subsequent visit data for analysis. Theinitial visit data and the subsequent visit data may be representativeof facial features, facial dimensions, and/or facial locations on theface of the individual, information related to U.S. Federal and/or stateor any other non-U.S. regulatory authority-identified criteria, whichmay include 3D facial and head topography data (e.g., linear, surfacearea, and volumetric data), the 3D image itself, 2D image measurements,a person's weight, age, body mass index (BMI), medical history, historyof surgeries and/or facial scars, facial dimensions, and/or otherinformation. In some embodiments, the initial visit data and subsequentvisit data each comprise millions of individual data points. In someembodiments, the numerical initial visit and subsequent visit data mayinclude data points, reference points, linear and surface areatopography, 2D data, volumetric data, etc., from the 3D facial imagesthat has been converted to numerical values for mathematical computationand analysis (e.g., as described herein).

Alignment component 806 is configured to identify facial referencepoints in the initial visit data and the subsequent visit data.Alignment component 806 is configured to determine whether the facialreference points in the initial visit data and the subsequent visit datameet alignment criteria. Alignment component 806 is configured to verifythat the RU in VISIT X+n is the same RU in VISIT X. For example, asdescribed above, the VISIT X+n numerical data may be compared to thedata from VISIT X to determine if common reference points (e.g., theforehead, upper portion of the nose and temples, see FIG. 7) areproperly aligned and matched (e.g., meet alignment criteria) to producevalid comparison results. Reference points can be chosen from universallandmarks which are unlikely to change (e.g. eye sockets) and/ortopographical landmarks with the least amount of tissue between the boneand skin (e.g. bridge of the nose). In some embodiments, the VECTRA H1and H2 (described herein) determine these reference pointsautomatically, for example. These devices are configured to determinethousands of reference points (e.g., if necessary) for VISIT-over-VISITcomparisons.

In some embodiments, alignment component 806 is configured to make aninitial determination as to whether an individual has reported (e.g.,made entries and/or selections via a user interface) weight changes,dental or cosmetic surgery, facial scarring, and/or other changes sincean initial or prior visit (e.g., VISIT X) that indicate improper (orlikely improper) alignment. This determination may be made based oninformation obtained by user information component 800 and/or otherinformation. Responsive to making such a determination, alignmentcomponent 806 may cause the system to indicate (e.g., via a userinterface of the system) that a new conventional fit test is required.

Fitting component 808 is configured to determine whether differencesbetween corresponding facial features, facial dimensions, and/or faciallocations on the face of the individual represented in the initial visitdata and subsequent visit data breach one or more pre-defined ADs.Fitting component 808 is configured to make this determination based onthe initial visit data and subsequent visit data, and/or otherinformation. Fitting component 808 is configured to make thisdetermination responsive to alignment component 808 determining that thefacial reference points meet the alignment criteria.

For example, as described above, if the common reference points areproperly aligned (e.g., the alignment criteria is met), the VISIT X+nnumerical data is compared to VISIT X data to determine if anydeviations or a conglomerate of those deviations are withinpre-determined ADs and/or other threshold values. The data from thebaseline visit (VISIT X) are compared to data collected during one ormore subsequent visits (VISITS X+n). Any individual data points orsubsets of data points that are compared are consistent,visit-over-visit (e.g., because the present system can track millions ofindividual data points on a face and find any of those points in asubsequent visit, even if the point has moved). If the mathematicaldeviations between the VISIT X numerical data and VISIT X+n numericaldata are equal to or less than the ADs, the RU is considered to have asuccessful fit test for the same manufacturer, model, and sizerespirator for an additional period of time (e.g., 12 months in theUnited States; longer period of time in other countries). If themathematical deviations between the VISIT X numerical data and VISIT X+nnumerical data are greater than the ADs, the RU is considered to have anunsuccessful fit test and must participate in a conventional QNFT orQLFT.

In some embodiments, fitting component 808 may be configured such thatthe numerical data representative of points on, and/or areas of, theface to be compared include those that come into contact with therespirator mask being evaluated, numerical data from points on, or areasof, the face that would indicate weight loss/gain, and/or numerical datafrom points on, or other areas, of the face. Fitting component 808 maybe configured to compare individual data points in the millions of datapoints of the initial visit data and subsequent visit data, one or moresubsets of data points, and/or other information. For example, fittingcomponent 808 may be configured to determine linear changes (point topoint), surface area changes (subsets of points), volumetric changes(subsets of points), and/other changes in the face of the individualbeing evaluated. Fitting component 808 may be configured to determine 3Dfacial and/or head topography changes (subsets of points), facialchanges based on properties of the 3D images themselves (point to pointand/or subsets of points), facial changes based on 2D image measurements(point to point and/or subsets of points), facial changes based on aperson's weight (point to point and/or subsets of points), facialchanges based on a person's age (point to point and/or subsets ofpoints), facial changes based on a person's race (point to point and/orsubsets of points), facial changes based on a person's body mass index(BMI) (point to point and/or subsets of points), facial changes based ona person's medical history (e.g., history of surgeries and/or facialscars) (point to point and/or subsets of points), and/or otherinformation.

In some embodiments, as described above, fitting component 808 isconfigured to generate a mask fit “pass” indication responsive todifferences between the corresponding facial features, facialdimensions, and/or facial locations on the face of the individualrepresented in the initial visit data and subsequent visit data notbreaching the one or more pre-defined ADs. Fitting component 808 may beconfigured to generate a mask fit “fail” indication responsive todifferences between the corresponding facial features, facialdimensions, and/or facial locations on the face of the individualrepresented in the initial visit data and subsequent visit databreaching the one or more pre-defined ADs.

In some embodiments, fitting component 808 is configured to generate thepass or fail indication based on a cumulative scoring of delta(difference) values tabulated by fitting component 808 across the face.For example, these may include delta values for various points and/orareas where a respirator mask comes into contact with the individual'sface, and/or numerous other (e.g., smaller) regions of the face. In someembodiments, fitting component 808 may be configured such that if anyone of the cumulative tabulated delta values are greater than thepredetermined ADs, a failed fit test is indicated. In some embodiments,fitting component 808 may be configured such that a failed fit test isindicated only if some predetermined combination of two or more of thecumulative tabulated delta values are greater than the predeterminedcorresponding ADs for those delta values. In some embodiments, a failedfit test may be indicated based on a cumulative score of the deltas forthe entire face, or a smaller subset of deltas from one or more limitedregions of the face.

By way of a non-limiting example, algorithms may be used to calculate a3D Fit Score (described below) and/or other scores using the numericalinitial visit data and subsequent visit data (e.g., as described above),which may include data from either RUs face in its entirety orsubsections of the face (note: individual data from the RU like, forexample, excessive weight gain or surgical history since the last fittest may produce a default “fail” notice). When scores from a subsequentvisit are compared to the scores associated with the baseline visit orother intervening visits, and the difference is greater than one or moreof the ADs, a test “fail” may be indicated.

In some embodiments, generating the “pass” or “fail” indications mayinclude causing the electronic recording of a pass or fail status inelectronic storage of the system, transmitting the pass or failindications to other systems, and/or other operations. In someembodiments, generating the pass or fail indications may include causingthe electronic recording or transmission of identifying information,respirator manufacturer, model, and/or a size of respirator tested.

As described above, in some embodiments, determining whether differences(e.g., deltas) between corresponding facial features, facial dimensions,and/or facial locations on the face of the individual represented in theinitial visit data and subsequent visit data breach the one or morepre-defined ADs comprises comparing individual data points in theinitial visit data to corresponding individual data points in thesubsequent visit data. Also as described above, in some embodiments,fitting component 808 is configured to determine whether differences(deltas) between corresponding facial features, facial dimensions,and/or facial locations on the face of the individual represented in theinitial visit data and subsequent visit data breach one or morepre-defined ADs by comparing a plurality of facial features, facialdimensions, and/or facial locations on the face of the individualrepresented in the initial visit data and subsequent visit data tocorresponding ADs for the individual facial features, facial dimensions,and/or facial locations. In some embodiments, this may comprisedetermining a weighted combination of the comparisons of the pluralityof facial features, facial dimensions, and/or facial locations on theface of the individual represented in the initial visit data andsubsequent visit data to the corresponding ADs for the individual facialfeatures, facial dimensions, and/or facial locations.

In some embodiments, fitting component 808 may be configured such thatADs are generated at manufacture of the present system, responsive toentries and/or selections made via a user interface by a user of thepresent system, based on a variance or interim order granted by Federaland/or state OSHA's and/or any other appropriate regulatory authority(e.g., as described above), and/or in other ways. In some embodiments,the ADs are generated based on prior facial measurements made on humanmodels, facial measurements made on a population of subjects over time,and/or other sources of information.

By way of a non-limiting example, ADs may be determined based on facialmeasurements from subjects expecting to lose weight over time. The datafrom one of these such subjects may be as follows:

3D FIT SUCCESSFUL FIT MODEL/ONE FIT DATE WEIGHT SCORE MODEL/SIZE FACTORSIZE SMALLER FACTOR 1/1/2018 250 lbs. 1.0 (baseline) 3M 1860/Lg 250 3M1860/Med 70 2/1/2018 240 lbs. 1.22 3M 1860/Lg 210 3M 1860/Med 753/1/2018 232 lbs. 1.28 3M 1860/Lg 180 3M 1860/Med 88 4/1/2018 222 lbs.1.33 3M 1860/Lg 89 3M 1860/Med 150 Xxxxxxxx 5/1/2018 220 lbs. 1.0(baseline) 3M 1860/Med 160 3M 1860/Sm 72

In the example above, on Apr. 1, 2018, the user went from a large to amedium respirator based on a minimum fit factor of 100 as mandated byFed OSHA CFR 1910.134 standards for a half-mask or quarter face piecerespirator.

As another non-limiting example, the ADs may be generated as follows: Abaseline score (noted in the above example as the “3D Fit Score”) may becalculated, as described above, when an RU is successfully fit tested toa specific manufacturer, model and size of respirator. The 3D Fit Scoresmay relate data as described above, which may include the RUs face inits entirety or subsections of the face. In subsequent tests,corresponding 3D Fit Scores may be tabulated. A “change event” may bedescribed as when the RUs fit factor drops below values prescribed inFed OSHA CFR 1910.134 and/or other standards, for example. When such achange event occurs, the difference between the corresponding 3D FitScore and the baseline score may be noted as the RU's Delta Value.Through research projects involving dozens of subjects, (e.g.,representative) RU's Delta Values may be collected, tabulated andaveraged (and/or manipulated with other mathematical transformations),and by using generally accepted statistical research practices, ADs maybe established.

As yet another non-limiting example, fitting component 808 may beconfigured such that ADs may be determined based on information from apopulation of subjects experiencing weight fluctuations and/or migratingskin, and/or other subjects. An individual subject (RU) may be fittedwith a respirator mask and 3D images and other data may be captured(e.g., VISIT X). Periodically, the subjects may be re-fit tested to thesame respirator (e.g., VISIT X-n). At these re-fit tests 3D images andother data may be collected (e.g., as described above). When an RUexperiences weight gain or loss, or enough skin migration to cause theRU to no longer fit their respirator mask (change event), additional 3Dimages may be taken and other corresponding data may be collected. Deltavalues associated with the change event may also be recorded. Thesedelta values for the population of RU's may be tabulated by fittingcomponent 808, and fitting component 808 may determine ADs based on thetabulated data.

In some embodiments, fitting component 808 may be configured such that aprocess for determining the AD's comprises multiple phases. In someembodiments, a first phase comprises facilitating data gathering usinghuman models (e.g., mannequins and/or other human models) anddetermining preliminary AD's based on the data gathered using the humanmodels. In some embodiments, a second phase comprises facilitating datagathering using a population of human subjects (e.g., as describedabove) and adjusting the preliminary AD's based on the data gatheredfrom the population of human subjects. Example details for each of thesephases are provided below. However, it should be noted that the numberof phases described herein is not intended to be limiting. More or lessphases may be used to determine the AD's described herein.

Phase I—Preliminary Allowable Deltas (AD): Human Models

Fitting component 808 may be configured such that Phase I comprisesdetermining a statistically valid population size using human models(e.g., mannequins and/or other human models). Once a population size isestablished, at a first fit test (e.g., VISIT X), individual humanmodels in the population are successfully fit tested with a respiratorusing conventional fit testing methods (e.g., as described above).Corresponding unique identifying data (e.g., including fit factor,weight data, and or other data) is recorded in the respirator user (RU)file for a given human model. Corresponding 3D images and 2D images ofeach model's headform are captured and saved in the RU file. Theseimages are converted to numerical data for mathematical analysis. Thenumerical data is also recorded in the RU file. The numerical data mayinclude angular measurements, point-to-point measurements, surfaceareas, face and/or head volume, and/or other information. By way of anon-limiting example, virtual (e.g., 343 cm³) cube external sectionvolumes, surface areas, and point-to-point distance data may be recordedas shown in Table 1, Table 3 and Table 5, respectively appended inEXAMPLE 1 below.

Example virtual cube external section volumes are illustrated in FIG.12. FIG. 12 illustrates two views 1200 and 1202 of volume of threeexample virtual cube external section volumes 1204 (Volume 1), 1206(Volume 2), and 1208 (Volume 3). In some embodiments (e.g., as shown inFIG. 12), Volumes 1 and 2 (1204 and 1206 may extend across an RU's 1210right and left cheeks 1212 and 1214 respectively. Volumes 1 and 2 (1204and 1206) may extend from edges 1216 and 1218 of a bridge 1220 of theRU's 1210 nose toward an ear 1222 of the RU 1210 at approximately eyelevel, and down across the RU's cheek toward the RU's chin 1224,terminating at approximately lip level. Volumes 1 and 2 may be similarlypositioned on RU 1210's face, but on the left and right sides of RU1210's. Volume 3 (1208) substantially surrounds the chin 1224 of RU1210, extending across the face of RU 1210 just below the bottom lip1230 of RU 1210. In this example, Volume 1, Volume 2, and Volume 3 areconfigured to be 343 cm³. In some embodiments, fitting component 808(FIG. 11) is configured to identify the facial features described abovebased on the information from the corresponding facial images, anddetermine the volumes. In some embodiments, the volumes 1, 2, and 3 maybe about 343 cm³, for example. The 343 cm³ (for example) is the volumeof a 7 cm×7 cm×7 cm cube. The increasing volume of a person losingweight, shown in the tables in EXAMPLE 1 below, is the volume of thecube OUTSIDE of the face. When the subject loses weight, the externalportion of the cube's volume increases. This example is not intended tobe limiting. Other facial virtual cube external section volumes may beused, the volumes may or may not be the same, more or less than threeseparate volumes may be used, and the volumes may not be positioned inthe locations shown in FIG. 12.

Example surface areas are illustrated in FIG. 13. FIG. 13 illustratestwo views 1300 and 1302 of areas 1304 (Area 1), 1306 (Area 2), 1308(Area 3), and 1310 (Area 4). Areas 1-4 are illustrated on a left side1312 of an RU 1210's face. Similar areas (shown but not labeled in FIG.13) on the right side 1314 of RU 1210's face may also be used. In someembodiments, Areas 1-4 (1304-1310) have a triangular shape with sidesthat radiate from the ear 1222 of RU 1210 across the face of RU 1210 andterminate at or near a centerline 1320 (e.g., that follows the bridge ofthe nose 1220 from the forehead 1350 of RU 1210 to chin 1224) of theface of RU 1210. In some embodiments, Area 1 (1304) may range from abouteye level 1352 of RU 1210 to a tip 1354 of the nose 1356 of RU 1210 andback to ear 1222 of RU 1210. Area 2 (1306) may range between tip 1354 ofnose 1356 of RU 1210, a center (approximately) of chin 1224, and back toear 1222. Area 3 (1308) may cover a side cheek area 1360 portion of theface of RU 1210, extending from the center of chin 1224, back along ajaw 1362 of RU 1210, and up to ear 1222. Area 4 (1310) may cover a rearjaw portion 1370 of RU 1210 near ear 1222 as shown in FIG. 13. In someembodiments, fitting component 808 (FIG. 11) is configured to identifythe facial features described above based on the information from thecorresponding facial images, and determine the areas. This example isnot intended to be limiting. Other facial areas may be used, the areasmay or may not be the same, more or less than four separate areas may beused, and the areas may not be positioned in the locations shown in FIG.13.

Example point-to-point distances are illustrated in FIG. 14. FIG. 14illustrates two views 1400 and 1402 of point-to-point distancesPTP1-PTP8. PTP1-PTP8 are shown with corresponding tracer lines 1404showing examples of possible movement of individual points 1406 on theface of RU 1210. In some embodiments, points 1406 may lie on lines thatdefine the borders of Areas 1-4 shown in FIG. 13. In some embodiments,fitting component 808 (FIG. 11) is configured to identify the facialfeatures described above based on the information from the correspondingfacial images, and determine the point-to-point distances. This exampleis not intended to be limiting. Other facial point-to-point distancesmay be used, the distances may or may not be the same, more or less thaneight separate (per side of an RU's face) distances may be used, and thedistances may not be positioned in the locations shown in FIG. 14.

Returning to FIG. 11 and the description of determining AD's as itrelates to fitting component 808, at a VISIT X+n, the individual humanmodel headforms are incrementally altered to increase or decrease facialvolume, mimicking weight loss or gain in an RU. After such alterations,the individual human models are fit tested with the respirator mask fromVISIT X, using conventional fit testing methods. If a human model issuccessfully fit tested, corresponding unique identifying data (e.g.,including fit factor, weight data, and/or other information) is recordedin the RU file. Corresponding 3D images and 2D images of eachincrementally-altered human model headform is captured and saved in theRU file. The images are converted to numerical data for mathematicalanalysis and recorded in the RU file. The numerical data may includeangular measurements, point-to-point measurements, surface areas, faceand/or head volume, and/or other information (e.g., measurements thatcorrespond to the measurements from VISIT X). By way of a non-limitingexample, virtual (e.g., 343 cm³) cube external section volumes, surfaceareas, and point-to-point distance data for multiple VISITS X+n arerecorded as shown in example Table 1, Table 3 and Table 5, respectivelyappended in EXAMPLE 1 below.

If a change event has occurred, (e.g., a mannequin can no longer besuccessfully fit tested to the respirator mask used in VISIT X usingconventional fit test methods), as described above, corresponding uniqueidentifying data (e.g., including fit factor, weight data, and/or otherinformation) is recorded in the RU file. In this example, a change eventoccurred at VISIT X+3. Corresponding unique identifying data (e.g.,including fit factor, weight data, and or other data) is recorded in therespirator user (RU) file for a given human model. Corresponding 3Dimages and 2D images of each model's headform are captured and saved inthe RU file. These images are converted to numerical data formathematical analysis. The numerical data is also recorded in the RUfile. The numerical data may include angular measurements,point-to-point measurements, surface areas, face and/or head volume,and/or other information. The percentage of change (e.g., the DeltaValue) from VISIT X is determined and recorded for the categories ofmeasurement (e.g., volume, area, point-to-point distance) as shown inTables 1 (VISIT X+3), 3 (VISIT X+3) and 5 (VISIT X+3) of EXAMPLE 1.

These delta values may be combined with other corresponding delta valuesfor other subjects (RUs) as shown in Tables 2, 4 and 6 of EXAMPLE 1 tofacilitate aggregation (e.g., by fitting component 808) of the humanmodel population's data. As shown in Table 2, the delta values for thevolume measurements for different subjects (RUs) may be listed in thesame table. In Table 4, the delta values for the area measurements fordifferent subjects (RUs) are listed. In Table 6, the delta values forthe point-to-point measurements for different subjects (RUs) are listed.In some embodiments, as shown in Tables 2, 4, and 6, mean, standarddeviation, and/or other values may be determined for the delta valuesfor the different types of measurements (volume, area, point-to-point inthis example).

These values and/or other information may be used (e.g., by fittingcomponent 808) to determine the ADs. For example, a preliminary AD(e.g., determined based on the mannequin data) for a given measurementmay comprise some function of an average (e.g., across the population ofhuman models) delta value (e.g., % change from VISIT X for a givenvolume, area, point-to-point distance, etc., measurement) thatcorresponds to a change event (e.g., a failed fit-test) plus or minus apredetermined number of standard deviations.

In some embodiments, an AD (preliminary or otherwise) may be calculatedas follows (the below example is directed to determining an AD for theVirtual Cube External Section Volume category of measurement, but may besimilarly applied to other measurements):AD volume 3 (weight gain or loss)=mean+((standard deviation×2)/2)

Using the information in Tables 1 and 2, the above calculation would be:AD volume 3 (weight loss)=20.34%+((0.5009×2)/2)=20.84%

It should be noted that this is just one example of determining onepreliminary AD. As described above, in some embodiments, fittingcomponent 808 is configured to determine whether differences (deltas)between corresponding facial features, facial dimensions, and/or faciallocations on the face of the individual; age data, weight data, BMIdata; and/or other facial or non-facial data; and/or other informationrepresented in the initial visit data and subsequent visit data breachone or more pre-defined ADs by comparing a plurality of facial features,facial dimensions, and/or facial locations on the face of theindividual; age data, weight data, BMI data; and/or other facial ornon-facial data; and/or other information represented in the initialvisit data and subsequent visit data to a corresponding plurality ofADs. In some embodiments, this may comprise determining a weightedcombination of AD's and/or other AD criteria.

Using conventional fit testing methods, a human model (e.g. a mannequin)may then be fit tested to the respirator mask identified in VISIT X, butone size smaller (e.g., for simulated weight loss) or one size larger(e.g., for simulated weight gain). Upon a successful fit test, thecorresponding unique identifying data (e.g., including fit factor,weight data, and or other data) is recorded in the respirator user (RU)file for a given human model. Corresponding 3D images and 2D images ofeach model's headform are captured and saved in the RU file. Theseimages are converted to numerical data for mathematical analysis. Thenumerical data is also recorded in the RU file. The numerical data mayinclude angular measurements, point-to-point measurements, surfaceareas, face and/or head volume, and/or other information. This data maybe recorded in Table 1, Table 3 and Table 5, respectively.

Phase II—Allowable Deltas (AD): Human Subjects

Fitting component 808 may be configured such that Phase II comprisesdetermining a statistically valid population size using human subjects(e.g., not mannequins and/or other human models). Once a population sizeis established, at a first fit test (e.g., VISIT X), individual subjectsin the population are successfully fit tested with a respirator usingconventional fit testing methods (e.g., as described above).Corresponding unique identifying data (e.g., including fit factor,weight data, and or other data) is recorded in the respirator user (RU)file for a given subject. Corresponding 3D images and 2D images of eachsubject's headform are captured and saved in the RU file. These imagesare converted to numerical data for mathematical analysis. The numericaldata is also recorded in the RU file. The numerical data may includeangular measurements, point-to-point measurements, surface areas, faceand/or head volume, and/or other information. By way of a non-limitingexample, virtual (e.g., 343 cm³) cube external section volumes, surfaceareas, and point-to-point distance data may be recorded as shown inTable 1, Table 3 and Table 5, respectively appended in EXAMPLE 1 below(e.g., the human subject data may be added to the human model dataand/or the human subject data may populate its own versions of Tables 1,3, and 5). (The mannequin (human model) data may be used as a frameworkto predict what the AD's will be on human subjects.)

At a VISIT X+n, the individual subjects' are fit tested with therespirator mask from VISIT X, using conventional fit testing methods. Ifa subject is successfully fit tested, corresponding unique identifyingdata (e.g., including fit factor, weight data, and/or other information)is recorded in the RU file. Corresponding 3D images and 2D images of thesubject's headform is captured and saved in the RU file. The images areconverted to numerical data for mathematical analysis and recorded inthe RU file. The numerical data may include angular measurements,point-to-point measurements, surface areas, face and/or head volume,and/or other information (e.g., measurements that correspond to themeasurements from VISIT X). By way of a non-limiting example, virtual(e.g., 343 cm³) cube external section volumes, surface areas, andpoint-to-point distance data for multiple VISITS X+n may be recorded asshown in example Table 1, Table 3 and Table 5, respectively appended inEXAMPLE 1 below (and/or similar tables).

If a change event has occurred, (e.g., a subject can no longer besuccessfully fit tested to the respirator mask used in VISIT X usingconventional fit test methods), as described above, corresponding uniqueidentifying data (e.g., including fit factor, weight data, and/or otherinformation) is recorded in the RU file. In this example, a change eventmay occur at VISIT X+3 (as described above for the mannequin modelsand/or at other times). Corresponding unique identifying data (e.g.,including fit factor, weight data, and or other data) is recorded in therespirator user (RU) file for a given subject. Corresponding 3D imagesand 2D images of each subject's headform are captured and saved in theRU file. These images are converted to numerical data for mathematicalanalysis. The numerical data is also recorded in the RU file. Thenumerical data may include angular measurements, point-to-pointmeasurements, surface areas, face and/or head volume, and/or otherinformation. The percentage of change (e.g., the Delta Value) from VISITX is determined and recorded for the categories of measurement (e.g.,volume, area, point-to-point distance) as shown in Tables 1 (VISIT X+3),3 (VISIT X+3) and 5 (VISIT X+3) of EXAMPLE 1.

The subject may then be fit tested using conventional fit testingmethods to the next smaller (e.g., for weight loss) or larger (e.g., forweight gain) size of the same respirator mask used for VISIT X.Corresponding images and information (e.g., as described above) may besaved in the RU file.

The delta values for fit tests that correspond to change events and/orother information may be used (e.g., by fitting component 808) tovalidate the preliminary ADs determined based on the human model data,adjust the ADs determined based on the human model data, and/ordetermine new ADs based on the data for the human subjects. For example,if a subject's percentage change (delta value) in a measurement category(e.g., volume, area, point-to-point distance, etc.) for a fit test thatcorresponds to a change event is greater than the preliminary AD forthat measurement determined based on the human model data (e.g., Phase Idescribed above), then the preliminary AD may be considered validated.In some embodiments, if the fit tests of the overall population ofsubjects is successfully correlated to the preliminary ADs with asensitivity level of <0.05 (for example), the preliminary AD(s) may beconsidered valid.

In some embodiments, the human subject population's average deltas andstandard deviations may be combined (e.g., with or without the humanmodel data) to determine ADs for any and/or all measurement categories.

This example should also be considered to extend to embodiments thatutilize a plurality of weighted ADs and/or other AD criteria. Forexample, the present method may include determining and using ADs forone or more categories of measurements. Separate ADs may be determinedfor weight and/or facial and/or head volume increases and volumedecreases, for example, because the areas where a respirator interactswith the skin is more adversely affected by weight loss than weightgain. In weight loss scenarios, faces are more likely to create concavefeatures, for example. On the other hand, in weight gain scenarios,facial features “fill out”, creating a better seal between therespirator and the face. In some embodiments, an aggregation of weightedADs for different categories of measurements, may be used to predictsuccessful or unsuccessful fit tests. The table below lists possibleweighting ranges for ADs related to various measurement categories.

Expected Ratios (weight loss or weight gain Category of Measurementscenarios) AD Volume 1   20-33.3% AD Volume 2   20-33.3% AD Volume 3  20-33.3% AD Point to Point (average AD)  6-20% AD Surface Area(average AD) 3-5% AD Age 3-5% AD Weight 10-20% AD Body Mass Index 3-5%TOTAL 100%

In some embodiments, fitting component 808 may be configured to adjustthe AD's until there is a 95% correlation (and/or other correlations)between the human model population and the human subjects.

These examples are not intended to be limiting. For example, data ispresented in tables (e.g., in EXAMPLE 1) to ease a reader'sunderstanding. The number of subjects (RUs) and the number and types ofmeasurements described are intended as example. It should also be notedthat aspects of any or all of these examples may be combined todetermine one or more AD's. Other examples are contemplated, anddevelopment is expected. These examples are meant to represent otherembodiments which one of ordinary skill in the art performing similaroperations would be motivated to produce by the spirit and scope of thedescribed examples (and the other examples described throughout thespecification).

In some embodiments, fitting component 808 may be configured tocategorize the face of the individual into a NIOSH Headform Categorybased on the initial visit data, the subsequent visit data, and/or thedifferences between the corresponding facial features, facialdimensions, and/or facial locations on the face of the individualrepresented in the initial visit data and subsequent visit data. In someembodiments, the NIOSH Headform Categories include small, medium, large,long/narrow, and short/wide. In some embodiments, fitting component 808may be configured to determine and/or adjust the one or more pre-definedADs based on the categorized NIOSH Headform Category (e.g., such thatthere are sets of ADs for individuals with different headforms).

In some embodiments, fitting component 808 may be configured todetermine a recommended respirator mask manufacturer and/or model andsize for the individual based on the initial visit data, the subsequentvisit data, the differences between the corresponding facial features,facial dimensions, and/or facial locations on the face of the individualrepresented in the initial visit data and subsequent visit data, theNIOSH Headform Category, and/or other information. In some embodiments,fitting component 808 may be configured to access one or more externaldatabases of mask manufacturer and model data (e.g., from one or morecooperating mask suppliers). In some embodiments, mask manufacturer andmodel data is stored by the present system. For example, maskmanufacturers may submit mask model data to the present system, where itmay be stored in an internal system database for later access.

In some embodiments, fitting component 808 may be configured todetermine, based on the differences between the corresponding facialfeatures, facial dimensions, and/or facial locations on the face of theindividual represented in the initial visit data and subsequent visitdata, presence of a temporary facial blemish. In such embodiments,fitting component 808 may be configured to adjust the determination ofwhether the differences between the corresponding facial features,facial dimensions, and/or facial locations on the face of the individualrepresented in the initial visit data and subsequent visit data breachthe one or more pre-defined ADs (e.g., to avoid and/or decreaseincorrect “pass” or “fail” fitting determinations). In some embodiments,determining a temporary facial blemish may be included in, and/or be anoutput of the computation and tabulation of the 3D Fit Score describedabove. For example, fitting component 808 may be able to determine thetemporary nature of a pimple, and adjust for the temporary nature of thepimple in the 3D Fit Score scoring. This adjustment may includeeliminating one or more ADs (e.g., an AD for the area of the face wherethe blemish is located), temporarily changing (e.g., reducing) theweight of an AD affected be the blemish in an algorithm, and/or makingother adjustments.

Prediction component 810 may be configured to make one or morepredictions related to the facial features, facial dimensions, and/orfacial locations on the face of the individual represented in theinitial visit data and subsequent visit data. For example, in someembodiments, prediction component 810 may be configured to determine oneor more rates of change for the corresponding facial features, facialdimensions, and/or facial locations on the face of the individualrepresented in the initial visit data and subsequent visit data.Prediction component 810 is configured to determine the rates of changebased on the differences between the corresponding facial features,facial dimensions, and/or facial locations on the face of the individualrepresented in the initial visit data and subsequent visit data, and/orother information. In such embodiments, prediction component 810 may beconfigured to predict an expected failure date when differences betweenthe corresponding facial features, facial dimensions, and/or faciallocations on the face of the individual represented in the initial visitdata and subsequent visit data will breach the one or more pre-definedADs. The expected failure date may be predicted based on the one or morepre-defined ADs and the one or more rates of change for thecorresponding facial features, facial dimensions, and/or faciallocations on the face of the individual represented in the initial visitdata and subsequent visit data, and/or other information (e.g., asdescribed above, based on a computed rate at which the 3D facial imagedata is approaching one or more ADs, an expected failure date can becomputed).

In some embodiments, prediction component 810 may be configured todetermine relationships between one or more physical parameters of anindividual being fitted for a mask and the differences between thecorresponding facial features, facial dimensions, and/or faciallocations on the face of the individual represented in the initial visitdata and subsequent visit data. For example, prediction component 810may be configured to determine a relationship between a weight of theindividual and the differences between the corresponding facialfeatures, facial dimensions, and/or facial locations on the face of theindividual represented in the initial visit data and subsequent visitdata. In such embodiments, prediction component 810 may be configured topredict, based on the relationship, a degree of weight gain and/or lossby the individual that will cause the differences between thecorresponding facial features, facial dimensions, and/or faciallocations on the face of the individual represented in data for futurevisits will breach the one or more pre-defined ADs.

In some embodiments, prediction component 810 may be configured todetermine relationships between one or more demographic parameters of anindividual being fitted for a mask and the differences between thecorresponding facial features, facial dimensions, and/or faciallocations on the face of the individual represented in the initial visitdata and subsequent visit data. For example, prediction component 810may be configured to determine a relationship between the age, race, orgender of the individual and the differences between the correspondingfacial features, facial dimensions, and/or facial locations on the faceof the individual represented in the initial visit data and subsequentvisit data. In such embodiments, prediction component 810 may beconfigured to predict, based on the demographic parameterrelationship(s), whether the differences between the correspondingfacial features, facial dimensions, and/or facial locations on the faceof the individual represented in data for future visits will breach theone or more pre-defined ADs.

In some embodiments, prediction component 810 may be configured topredict or otherwise determine the one or more medical conditionsexperienced by an individual being fitted for a respirator mask. Forexample, prediction component 810 may be configured to determine, basedon the differences between the corresponding facial features, facialdimensions, and/or facial locations on the face of the individualrepresented in the initial visit data and subsequent visit data,presence of skin cancer on the face of the individual. As anotherexample, prediction component 810 may be configured to predict orotherwise determine, based on data collected from images of the RUs eyesand/or the differences between the corresponding facial features, facialdimensions, and/or facial locations on the face of the individualrepresented in the initial visit data and subsequent visit data, thepossible presence of heart disease in the individual. As even anotherexample, prediction component 810 may be configured to predict and/orotherwise determine, based on the differences between the correspondingfacial features, facial dimensions, and/or facial locations on the faceof the individual represented in the initial visit data and subsequentvisit data, presence of asymmetric skin migration indicative of astroke, or Bell's Palsy in the individual.

In some embodiments, prediction component 810 may be configured topredict or recommend a respirator mask manufacturer and/or model for adifferent individual (e.g., an individual who has not yet begun atypical mask fitting process). Prediction component may be configured topredict or recommend a respirator manufacturer, model and size based onthe manufacturers' specifications for each respirator, the initial visitdata, the subsequent visit data, and/or the differences between thecorresponding facial features, facial dimensions, and/or faciallocations on the face of the individual represented in the initial visitdata and subsequent visit data. In some embodiments, the recommendedrespirator mask manufacturer and/or model for the different individualmay be predicted based on (1) the initial visit data, the subsequentvisit data, and/or the differences between the corresponding facialfeatures, facial dimensions, and/or facial locations on the face of theindividual represented in the initial visit data and subsequent visitdata; (2) the relationship between a weight of the individual and thedifferences between the corresponding facial features, facialdimensions, and/or facial locations on the face of the individualrepresented in the initial visit data and subsequent visit data (e.g.,for other individuals with similar weights or weight changes); (3) therelationship between the demographic information of the individual andthe differences between the corresponding facial features, facialdimensions, and/or facial locations on the face of the individualrepresented in the initial visit data and subsequent visit data (e.g.,for individuals with similar demographics), and/or other information.

In some embodiments, prediction component 810 may be configured suchthat making one or more predictions related to the facial features,facial dimensions, and/or facial locations on the face of the individualrepresented in the initial visit data and subsequent visit datacomprises causing one or more machine-learning models to be trainedusing the initial visit data and subsequent visit data, the informationobtained by user information component 800, and/or other information. Insome embodiments, the machine-learning model is trained based on theinitial visit data and subsequent visit data by providing the initialvisit data and subsequent visit data as input to the machine-learningmodel. In some embodiments, the machine-learning model may be and/orinclude mathematical equations, algorithms, plots, charts, networks(e.g., neural networks), and/or other tools and machine-learning modelcomponents. For example, the machine-learning model may be and/orinclude one or more neural networks having an input layer, an outputlayer, and one or more intermediate or hidden layers. In someembodiments, the one or more neural networks may be and/or include deepneural networks (e.g., neural networks that have one or moreintermediate or hidden layers between the input and output layers).

As an example, neural networks may be based on a large collection ofneural units (or artificial neurons). Neural networks may loosely mimicthe manner in which a biological brain works (e.g., via large clustersof biological neurons connected by axons). Each neural unit of a neuralnetwork may be connected with many other neural units of the neuralnetwork. Such connections can be enforcing or inhibitory in their effecton the activation state of connected neural units. In some embodiments,each individual neural unit may have a summation function that combinesthe values of all its inputs together. In some embodiments, eachconnection (or the neural unit itself) may have a threshold functionsuch that a signal must surpass the threshold before it is allowed topropagate to other neural units. These neural network systems may beself-learning and trained, rather than explicitly programmed, and canperform significantly better in certain areas of problem solving, ascompared to traditional computer programs. In some embodiments, neuralnetworks may include multiple layers (e.g., where a signal pathtraverses from front layers to back layers). In some embodiments, backpropagation techniques may be utilized by the neural networks, whereforward stimulation is used to reset weights on the “front” neuralunits. In some embodiments, stimulation and inhibition for neuralnetworks may be more free flowing, with connections interacting in amore chaotic and complex fashion.

For example, prediction component 810 may be configured such that atrained neural network is caused to indicate the expected failure datethe one or more pre-defined ADs will be breached (e.g., based on therates of change described above); the degree of weight gain and/or lossby the individual that will cause breach of the one or more pre-definedADs; whether the individual has heart disease, asymmetric skin migrationindicative of stroke, or Bell's Palsy; the recommended mask manufacturerand/or model; and/or other information.

In some embodiments, the operations performed by the componentsdescribed above may be repeated for subsequent mask fitting visits. Forexample, fitting component 808 may compare data for a series of maskfitting visits (e.g., data from VISIT X is compared to VISIT X+1, and/orVISIT X+2, . . . and/or VISIT X+n). In some embodiments, the operationsperformed by the components described above may be performed for animmediately prior visit (e.g., not necessarily an initial visit) and/orone or more subsequent visits. For example, fitting component 808 maycompare data for any two or more visits in a series of mask fittingvisits (e.g., data from any of VISIT X, VISIT X+1, VISIT X+2, . . .and/or VISIT X+n may be compared to any other one of VISIT X+1, VISITX+2, . . . and/or VISIT X+n that occurs subsequent in time). One ofordinary skill in the art will understand that other variations arepossible and this example is not limited to fitting component 808 only.

The reader should appreciate that the present application describesseveral inventions. Rather than separating those inventions intomultiple isolated patent applications, applicants have grouped theseinventions into a single document because their related subject matterlends itself to economies in the application process. However, thedistinct advantages and aspects of such inventions should not beconflated. In some cases, embodiments address all of the deficienciesnoted herein, but it should be understood that the inventions areindependently useful, and some embodiments address only a subset of suchproblems or offer other, unmentioned benefits that will be apparent tothose of skill in the art reviewing the present disclosure. Due to costconstraints, some inventions disclosed herein may not be presentlyclaimed and may be claimed in later filings, such as continuationapplications or by amending the present claims. Similarly, due to spaceconstraints, neither the Abstract nor the Summary of the Inventionsections of the present document should be taken as containing acomprehensive listing of all such inventions or all aspects of suchinventions.

It should be understood that the description and the drawings are notintended to limit the invention to the particular form disclosed, but tothe contrary, the intention is to cover all modifications, equivalents,and alternatives falling within the spirit and scope of the presentinvention as defined by the appended claims. Further modifications andalternative embodiments of various aspects of the invention will beapparent to those skilled in the art in view of this description.Accordingly, this description and the drawings are to be construed asillustrative only and are for teaching those skilled in the art thegeneral manner of carrying out the invention. It is to be understoodthat the forms of the invention shown and described herein are to betaken as examples of embodiments. Elements and materials may besubstituted for those illustrated and described herein, parts andprocesses may be reversed or omitted, and certain features of theinvention may be utilized independently, all as would be apparent to oneskilled in the art after having the benefit of this description of theinvention. Changes may be made in the elements described herein withoutdeparting from the spirit and scope of the invention as described in thefollowing claims. Headings used herein are for organizational purposesonly and are not meant to be used to limit the scope of the description.

As used throughout this application, the word “may” is used in apermissive sense (i.e., meaning having the potential to), rather thanthe mandatory sense (i.e., meaning must). The words “include”,“including”, and “includes” and the like mean including, but not limitedto. As used throughout this application, the singular forms “a,” “an,”and “the” include plural referents unless the content explicitlyindicates otherwise. Thus, for example, reference to “an element” or “aelement” includes a combination of two or more elements, notwithstandinguse of other terms and phrases for one or more elements, such as “one ormore.” The term “or” is, unless indicated otherwise, non-exclusive,i.e., encompassing both “and” and “or.” Terms describing conditionalrelationships, e.g., “in response to X, Y,” “upon X, Y,”, “if X, Y,”“when X, Y,” and the like, encompass causal relationships in which theantecedent is a necessary causal condition, the antecedent is asufficient causal condition, or the antecedent is a contributory causalcondition of the consequent, e.g., “state X occurs upon condition Yobtaining” is generic to “X occurs solely upon Y” and “X occurs upon Yand Z.” Such conditional relationships are not limited to consequencesthat instantly follow the antecedent obtaining, as some consequences maybe delayed, and in conditional statements, antecedents are connected totheir consequents, e.g., the antecedent is relevant to the likelihood ofthe consequent occurring. Statements in which a plurality of attributesor functions are mapped to a plurality of objects (e.g., one or moreprocessors performing steps A, B, C, and D) encompasses both all suchattributes or functions being mapped to all such objects and subsets ofthe attributes or functions being mapped to subsets of the attributes orfunctions (e.g., both all processors each performing steps A-D, and acase in which processor 1 performs step A, processor 2 performs step Band part of step C, and processor 3 performs part of step C and step D),unless otherwise indicated. Further, unless otherwise indicated,statements that one value or action is “based on” another condition orvalue encompass both instances in which the condition or value is thesole factor and instances in which the condition or value is one factoramong a plurality of factors. Unless otherwise indicated, statementsthat “each” instance of some collection have some property should not beread to exclude cases where some otherwise identical or similar membersof a larger collection do not have the property, i.e., each does notnecessarily mean each and every. Limitations as to sequence of recitedsteps should not be read into the claims unless explicitly specified,e.g., with explicit language like “after performing X, performing Y,” incontrast to statements that might be improperly argued to imply sequencelimitations, like “performing X on items, performing Y on the X'editems,” used for purposes of making claims more readable rather thanspecifying sequence. Statements referring to “at least Z of A, B, andC,” and the like (e.g., “at least Z of A, B, or C”), refer to at least Zof the listed categories (A, B, and C) and do not require at least Zunits in each category. Unless specifically stated otherwise, asapparent from the discussion, it is appreciated that throughout thisspecification discussions utilizing terms such as “processing,”“computing,” “calculating,” “determining” or the like refer to actionsor processes of a specific apparatus, such as a special purpose computeror a similar special purpose electronic processing/computing device.

The Abstract of the Disclosure is provided to allow the reader toquickly ascertain the nature of the technical disclosure. It issubmitted with the understanding that it will not be used to interpretor limit the scope or meaning of the claims. In addition, in theforegoing Detailed Description, it can be seen that various features aregrouped together in a single embodiment for the purpose of streamliningthe disclosure. This method of disclosure is not to be interpreted asreflecting an intention that the claimed embodiments require morefeatures than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive subject matter lies in less than allfeatures of a single disclosed embodiment. Thus, the following claimsare hereby incorporated into the Detailed Description, with each claimstanding on its own as a separate embodiment.

The present techniques will be better understood with reference to thefollowing enumerated embodiments:

1. A method for performing automated respirator mask fit testing, themethod comprising: obtaining at least one initial three-dimensional (3D)facial image of an individual from an initial respirator mask fittingvisit; obtaining at least one current 3D facial image of the individualfrom a subsequent respirator mask fitting visit; converting the initialfacial image and the current facial image to numerical initial visitdata and subsequent visit data for analysis, the initial visit data andthe subsequent visit data representative of facial features, facialdimensions, and/or facial locations on the face of the individual;identifying facial reference points in the initial visit data and thesubsequent visit data; determining whether the facial reference pointsin the initial visit data and the subsequent visit data meet alignmentcriteria; and responsive to a determination that the facial referencepoints in the initial visit data and the subsequent visit data meet thealignment criteria: determining, based on the initial visit data andsubsequent visit data, whether differences between corresponding facialfeatures, facial dimensions, and/or facial locations on the face of theindividual represented in the initial visit data and subsequent visitdata breach one or more pre-defined allowable deltas (ADs); andgenerating a mask fit pass indication responsive to differences betweenthe corresponding facial features, facial dimensions, and/or faciallocations on the face of the individual represented in the initial visitdata and subsequent visit data not breaching the one or more pre-definedADs; or generating a mask fit fail indication responsive to differencesbetween the corresponding facial features, facial dimensions, and/orfacial locations on the face of the individual represented in theinitial visit data and subsequent visit data breaching the one or morepre-defined ADs.2. The method of embodiment 1, further comprising determining, based onthe differences between the corresponding facial features, facialdimensions, and/or facial locations on the face of the individualrepresented in the initial visit data and subsequent visit data, one ormore rates of change for the corresponding facial features, facialdimensions, and/or facial locations on the face of the individualrepresented in the initial visit data and subsequent visit data.3. The method of embodiment 2, further comprising predicting, based onthe one or more pre-defined ADs and the one or more rates of change forthe corresponding facial features, facial dimensions, and/or faciallocations on the face of the individual represented in the initial visitdata and subsequent visit data, an expected failure date whendifferences between the corresponding facial features, facialdimensions, and/or facial locations on the face of the individualrepresented in the initial visit data and subsequent visit data willbreach the one or more pre-defined ADs.4. The method of any one of embodiments 1-3, further comprisingobtaining weight information for the individual at the initialrespirator mask fitting visit and the subsequent respirator mask fittingvisit; determining a relationship between a weight of the individual andthe differences between the corresponding facial features, facialdimensions, and/or facial locations on the face of the individualrepresented in the initial visit data and subsequent visit data; andpredicting, based on the relationship, a degree of weight gain and/orloss by the individual that will cause the differences between thecorresponding facial features, facial dimensions, and/or faciallocations on the face of the individual represented in the initial visitdata and subsequent visit data to breach the one or more pre-definedADs.5. The method of any one of embodiments 1-4, further comprisingcategorizing the face of the individual into a NIOSH Headform Categorybased on the initial visit data, the subsequent visit data, and/or thedifferences between the corresponding facial features, facialdimensions, and/or facial locations on the face of the individualrepresented in the initial visit data and subsequent visit data; anddetermining the one or more pre-defined ADs based on the categorizedNIOSH Headform Category.6. The method of embodiment 5, wherein NIOSH Headform Categories includesmall, medium, large, long/narrow, and short/wide.7. The method of any of embodiments 1-6, further comprising determininga recommended respirator mask manufacturer and/or model for theindividual based on the initial visit data, the subsequent visit data,and/or the differences between the corresponding facial features, facialdimensions, and/or facial locations on the face of the individualrepresented in the initial visit data and subsequent visit data.8. The method of any of embodiments 1-7, further comprising obtainingdemographic information for the individual at the initial respiratormask fitting visit and/or the subsequent respirator mask fitting visit,the demographic information comprising one or more of geographicalinformation about a location of the individual, racial information aboutthe individual, information about a gender of the individual,information about an industry where the individual works, or publichealth information related to the industry where the individual works.9. The method of embodiment 8, further comprising determining arelationship between the demographic information of the individual andthe differences between the corresponding facial features, facialdimensions, and/or facial locations on the face of the individualrepresented in the initial visit data and subsequent visit data; andpredicting based on the relationship, whether the differences betweenthe corresponding facial features, facial dimensions, and/or faciallocations on the face of the individual represented in future visit datawill breach the one or more pre-defined ADs.10. The method of any of embodiments 1-9, further comprising determiningbased on the differences between the corresponding facial features,facial dimensions, and/or facial locations on the face of the individualrepresented in the initial visit data and subsequent visit data,presence of a temporary facial blemish; and adjusting based on thedetermination of the presence of a facial blemish, the determination ofwhether the differences between the corresponding facial features,facial dimensions, and/or facial locations on the face of the individualrepresented in the initial visit data and subsequent visit data breachthe one or more pre-defined ADs.11. The method of any of embodiments 1-10, further comprisingdetermining, based on the differences between the corresponding facialfeatures, facial dimensions, and/or facial locations on the face of theindividual represented in the initial visit data and subsequent visitdata, presence of skin cancer on the face of the individual.12. The method of any of embodiments 1-11, further comprisingdetermining, based on the differences between the corresponding facialfeatures, facial dimensions, and/or facial locations on the face of theindividual represented in the initial visit data and subsequent visitdata, presence of heart disease in the individual.13. The method of any of embodiments 1-12, further comprisingdetermining, based on the differences between the corresponding facialfeatures, facial dimensions, and/or facial locations on the face of theindividual represented in the initial visit data and subsequent visitdata, presence of asymmetric skin migration indicative of a stroke orBell's Palsy in the individual.14. The method of any of embodiments 1-13, further comprisingdetermining a recommended respirator mask manufacturer and/or model fora different individual based on the initial visit data, the subsequentvisit data, and/or the differences between the corresponding facialfeatures, facial dimensions, and/or facial locations on the face of theindividual represented in the initial visit data and subsequent visitdata.15. The method of any of embodiments 1-14, further comprising obtainingweight information for the individual at the initial respirator maskfitting visit and the subsequent respirator mask fitting visit;determining a relationship between a weight of the individual and thedifferences between the corresponding facial features, facialdimensions, and/or facial locations on the face of the individualrepresented in the initial visit data and subsequent visit data;obtaining demographic information for the individual at the initialrespirator mask fitting visit and/or the subsequent respirator maskfitting visit, the demographic information comprising one or more ofgeographical information about a location of the individual, racialinformation about the individual, information about a gender of theindividual, information about an industry where the individual works, orpublic health information related to the industry where the individualworks; determining, a relationship between the demographic informationof the individual and the differences between the corresponding facialfeatures, facial dimensions, and/or facial locations on the face of theindividual represented in the initial visit data and subsequent visitdata; and determining the recommended respirator mask manufacturerand/or model for the different individual based on (1) the initial visitdata, the subsequent visit data, and/or the differences between thecorresponding facial features, facial dimensions, and/or faciallocations on the face of the individual represented in the initial visitdata and subsequent visit data; (2) the relationship between a weight ofthe individual and the differences between the corresponding facialfeatures, facial dimensions, and/or facial locations on the face of theindividual represented in the initial visit data and subsequent visitdata; and (3) the relationship between the demographic information ofthe individual and the differences between the corresponding facialfeatures, facial dimensions, and/or facial locations on the face of theindividual represented in the initial visit data and subsequent visitdata.16. The method of any of embodiments 1-15, wherein determining, based onthe initial visit data and subsequent visit data, whether differencesbetween corresponding facial features, facial dimensions, and/or faciallocations on the face of the individual represented in the initial visitdata and subsequent visit data breach one or more pre-defined ADscomprises comparing a plurality of facial features, facial dimensions,and/or facial locations on the face of the individual represented in theinitial visit data and subsequent visit data to corresponding ADs forindividual facial features, facial dimensions, and/or facial locations.17. The method of any of embodiments 1-16, wherein determining whetherdifferences between corresponding facial features, facial dimensions,and/or facial locations on the face of the individual represented in theinitial visit data and subsequent visit data breach one or more of thepre-defined ADs comprises determining a weighted combination of thecomparisons of the plurality of facial features, facial dimensions,and/or facial locations on the face of the individual represented in theinitial visit data and subsequent visit data to the corresponding ADsfor the individual facial features, facial dimensions, and/or faciallocations.18. The method of any of embodiments 1-17, wherein the initial visitdata and subsequent visit data each comprise millions of individual datapoints, and determining whether differences between corresponding facialfeatures, facial dimensions, and/or facial locations on the face of theindividual represented in the initial visit data and subsequent visitdata breach the one or more pre-defined ADs comprises comparingindividual data points in the initial visit data to correspondingindividual data points in the subsequent visit data.19. The method of any of embodiments 1-18, wherein determining whetherdifferences between corresponding facial features, facial dimensions,and/or facial locations on the face of the individual represented in theinitial visit data and subsequent visit data breach one or more of thepre-defined ADs comprises determining at least one initial facial volumeand at least one subsequent facial volume of the face of the individualrepresented in the initial visit data and subsequent visit data andcomparing a difference between the at least one subsequent facial volumeand the at least one initial facial volume to a corresponding AD forfacial volume.20. The method of any of embodiments 1-19, wherein determining whetherdifferences between corresponding facial features, facial dimensions,and/or facial locations on the face of the individual represented in theinitial visit data and subsequent visit data breach one or more of thepre-defined ADs comprises determining at least one initial facial areaand at least one subsequent facial area of the face of the individualrepresented in the initial visit data and subsequent visit data andcomparing a difference between the at least one subsequent facial areaand the at least one initial facial area to a corresponding AD forfacial area.21. The method of any of embodiments, 1-20, wherein determining whetherdifferences between corresponding facial features, facial dimensions,and/or facial locations on the face of the individual represented in theinitial visit data and subsequent visit data breach one or more of thepre-defined ADs comprises determining at least one initial facial pointto point distance and at least one subsequent facial point to pointdistance of the face of the individual represented in the initial visitdata and subsequent visit data and comparing a difference between the atleast one subsequent facial point to point distance and the at least oneinitial facial point to point distance to a corresponding AD for facialpoint to point distance.22. The method of any of embodiments 1-21, further comprisingdetermining the one or more pre-defined ADs by: obtaining at least onefirst fit test two-dimensional (2D) or three-dimensional (3D) facialimage of a plurality of human or human model test subjects in astatistically significant sample size of human or human model testsubjects; obtaining at least one second fit test two-dimensional (2D) orthree-dimensional (3D) facial image of the plurality of human or humanmodel test subjects in the statistically significant sample size ofhuman or human model test subjects, wherein faces of the plurality ofhuman or human model test subjects are changed between the first fittest and the second fit test converting the first and second fit testfacial images of the plurality of human or human model test subjects tonumerical first and second fit test data for analysis, the first andsecond fit test data representative of facial features, facialdimensions, and/or facial locations on the faces of the plurality ofhuman or human model test subjects; and for those human or human modeltest subjects in the plurality of human or human model test subjects whoexperience a change event between the first and second fit test,aggregating the first and second fit test data to determine the one ormore pre-defined ADs for the facial features, facial dimensions, and/orfacial locations on the faces of the plurality of human or human modeltest subjects.23. The method of embodiment 22, wherein a change event comprises aneven after which a human or human model test subject can no longer besuccessfully fit tested at the second fit test to a respirator mask usedin the first fit test using conventional fit test methods.24. The method of embodiment 22 or 23, wherein aggregating the first andsecond fit test data to determine the one or more pre-defined ADscomprises determining averages and standard deviations of differences inmeasurements represented by the numerical first and second fit test datacorresponding to the facial features, facial dimensions, and/or faciallocations on the faces of the plurality of human or human model testsubjects, and determining the one or more pre-defined ADs based on theaverages and standard deviations of the differences.25. The method of any of embodiments 22-24, further comprisingvalidating the one or more pre-defined ADs with fit test data for aplurality of actual respirator users (RU) who experience a change eventbetween fit tests.26. The method of any of embodiments 1-25, wherein generating the maskfit pass indication responsive to differences between the correspondingfacial features, facial dimensions, and/or facial locations on the faceof the individual represented in the initial visit data and subsequentvisit data not breaching the one or more pre-defined ADs; or generatingthe mask fit fail indication responsive to differences between thecorresponding facial features, facial dimensions, and/or faciallocations on the face of the individual represented in the initial visitdata and subsequent visit data breaching the one or more pre-defined ADsis performed for two or more different types of respirator masks usingthe same initial visit data and subsequent visit data.27. A tangible, non-transitory, machine-readable medium storinginstructions that when executed effectuate operations including: themethod of any one of embodiments 1-25.28. A system comprising one or more processors and memory storinginstructions that when executed by the processors cause the processorsto effectuate operations comprising: using the method of any one ofembodiments 1-25.

Example 1

TABLE 1 Virtual Cube, External Section Volume SUBJECT 001 Volume 1Volume 2 Volume 3 VISIT X 162.8 cm³ 162.3 cm³ 110.1 cm³ VISIT X + 1177.1 cm³ 177.3 cm³ 119.82 cm³  VISIT X + 2 187.1 cm³ 187.4 cm³ 129.1cm³ VISIT X + 3* 194.2 cm³ 194.6 cm³ 133.2 cm³ (Delta Value) (19.28%)(19.9%) (20.98%) *indicates a change event

TABLE 2 Population's Delta Values (Volume) Volume 1 Delta Volume 2 DeltaVolume 3 POPULATION Values Values Delta Values Subject 001 19.28%  19.9%20.98% Subject 002 19.22% 19.44% 20.22% Subject 003 20.01% 20.01% 20.56%Subject 004 19.02% 19.11% 20.03% Subject 005 19.33% 19.33% 19.87%Subject 006 20.22% 20.22% 19.55% Subject 007 20.04% 20.45% 20.04%Subject 008 19.99% 19.34% 21.01% Subject 009 19.44% 19.67% 20.22%Subject 010 19.55% 19.55% 20.88% MEAN DELTA 19.61%  19.7% 20.34%STANDARD .4192 .4313 .5009 DEVIATION ALLOWABLE 20.45% 20.56% 21.34%DELTAS (VOLUME)

TABLE 3 Surface Area SUBJECT 001 Area 1 Area 2 Area 3 Area 4 VISIT X 56cm²   54 cm²   51 cm² 53.2 cm² VISIT X + 1 55.07 cm²    53.1 cm² 50.2cm² 52.7 cm² VISIT X + 2 55 cm² 53.04 cm² 50.1 cm²   52 cm² VISIT X + 3*54.2 cm²   52.25 cm² 49.2 cm² 51.7 cm² (Delta Value) (3.2%) (3.2%)(3.5%) (2.8%) *indicates a change event

TABLE 4 Populations Delta Values (Surface Area) Surface Surface SurfaceSurface Area 1 Area 2 Area 3 Area 4 POPULATION Delta Values Delta ValuesDelta Values Delta Values Subject 001 3.2% 3.2% 3.5% 2.8% Subject 0022.6% 3.4% 3.5% 3.3% Subject 003 2.0% 2.5% 2.6% 2.7% Subject 004 3.4%2.8% 2.4% 2.6% Subject 005 2.5% 2.7% 2.0% 3.4% Subject 006 2.7% 3.5%3.3% 3.3% Subject 007 3.3% 2.4% 2.6% 2.6% Subject 008 2.9% 2.4% 2.4%3.4% Subject 009 2.8% 2.5% 2.6% 3.5% Subject 010 3.7% 3.3% 3.2% 2.4%MEAN DELTA 2.51%  2.48%  2.38%  3.45%  STANDARD .499 .461 .454 .416DEVIATION ALLOWABLE 3.01% 2.94% 2.75% 3.87% DELTAS (SURFACE AREA)

TABLE 5 Point-to-Point Distances (PTP) SUBJECT 001 PTP 1 PTP 2 PTP 3 PTP4 PTP 5 PTP 6 PTP 7 PTP 8 VISIT X 5.5 mm 5.3 mm 5.0 mm 5.2 mm 5.6 mm 5.3mm 5.0 mm 5.3 mm VISIT X + 1 5.7 mm 5.3 mm 5.2 mm 5.7 mm 5.7 mm 5.4 mm5.22 mm 5.7 mm VISIT X + 2   6 mm 5.4 mm 5.5 mm 5.9 mm 6.1 mm 5.4 mm5.88 mm 6.0 mm VISIT X + 3* 6.2 mm 5.5 mm 5.8 mm 5.9 mm 6.2 mm 5.7 mm5.9 mm 6.1 mm (Delta Value) (12.7%) (3.7%) (16%) (13.5%) (10.7%) (7.5%)(18%) (15.1%) *indicates a change event

TABLE 6 Population's Delta Values (Point-to-Point) PTP 1 PTP 2 PTP 3 PTP4 PTP 5 PTP 6 PTP 7 PTP 8 Delta Delta Delta Delta Delta Delta DeltaDelta POPULATION Values Values Values Values Values Values Values ValuesSubject 001  12.7%  3.7%    16%  13.5% 10.7%  7.5%    17%  15.1% Subject002  13.7%    3%  15.3%  13.7% 10.6%  7.4%    15%  13.4% Subject 003   12%  2.4%  16.4%    17% 10.0%  7.5%  16.6%  17.6% Subject 004  11.9% 3.4%  14.5%  16.6% 11.4%  6.8%    14%    16% Subject 005    12%    4%   10%  14.4% 10.5%  6.7%  10.6%  14.3% Subject 006    14%  3.3%  13.1%   13% 11.7%  7.5%  13.5%    13% Subject 007  10.9%  3.7%    16%    16%  11%  7.4%    16%  16.8% Subject 008  12.1%  3.1%  14.3%  14.5%   10% 7.4%    14%  14.4% Subject 009  11.8%  2.8%  16.3%  15.5% 11.1%  6.5% 16.3%    15% Subject 010    12%  3.7%    12%  14.2%   10%  6.3%  12.2% 14.1% MEAN DELTA  12.3%  3.3%  14.4%  14.8% 10.7%  7.1%  14.5%  15.0%STANDARD .924 .16 2.18 1.34 .60 .49 1.94 1.46 DEVIATION ALLOWABLE 13.22%3.46% 16.58% 16.14% 11.3% 7.59% 16.44% 16.46% DELTAS (POINT-TO- POINT)

What is claimed is:
 1. A method for performing automated respirator maskfit testing, the method comprising: obtaining, with one or moreprocessors, at least one initial two-dimensional (2D) orthree-dimensional (3D) facial image of an individual from an initialrespirator mask fitting visit; obtaining, with the one or moreprocessors, at least one current 2D or 3D facial image of the individualfrom a subsequent respirator mask fitting visit; converting, with theone or more processors, the initial facial image and the current facialimage to numerical initial visit data and subsequent visit data foranalysis, the initial visit data and the subsequent visit datarepresentative of facial features, facial dimensions, and/or faciallocations on the face of the individual; identifying, with the one ormore processors, facial reference points in the initial visit data andthe subsequent visit data; determining, with the one or more processors,whether the facial reference points in the initial visit data and thesubsequent visit data meet alignment criteria; and responsive to adetermination that the facial reference points in the initial visit dataand the subsequent visit data meet the alignment criteria: determining,with the one or more processors, based on the initial visit data andsubsequent visit data, whether differences between corresponding facialfeatures, facial dimensions, and/or facial locations on the face of theindividual represented in the initial visit data and subsequent visitdata breach one or more pre-defined allowable deltas (ADs), a given ADcomprising a threshold representative of a maximum amount of change in acorresponding facial feature, dimension, or location that would preventa respirator mask from fitting the face of the individual; andgenerating, with the one or more processors, a mask fit pass indicationresponsive to differences between the corresponding facial features,facial dimensions, and/or facial locations on the face of the individualrepresented in the initial visit data and subsequent visit data notbreaching the one or more pre-defined ADs; or generating, with the oneor more processors, a mask fit fail indication responsive to differencesbetween the corresponding facial features, facial dimensions, and/orfacial locations on the face of the individual represented in theinitial visit data and subsequent visit data breaching the one or morepre-defined ADs.
 2. The method of claim 1, further comprisingdetermining, with the one or more processors, based on the differencesbetween the corresponding facial features, facial dimensions, and/orfacial locations on the face of the individual represented in theinitial visit data and subsequent visit data, one or more rates ofchange for the corresponding facial features, facial dimensions, and/orfacial locations on the face of the individual represented in theinitial visit data and subsequent visit data.
 3. The method of claim 2,further comprising predicting, with the one or more processors, based onthe one or more pre-defined ADs and the one or more rates of change forthe corresponding facial features, facial dimensions, and/or faciallocations on the face of the individual represented in the initial visitdata and subsequent visit data, an expected failure date whendifferences between the corresponding facial features, facialdimensions, and/or facial locations on the face of the individualrepresented in the initial visit data and subsequent visit data willbreach the one or more pre-defined ADs.
 4. The method of claim 1,further comprising obtaining, with the one or more processors, weightinformation for the individual at the initial respirator mask fittingvisit and the subsequent respirator mask fitting visit; determining,with the one or more processors, a relationship between a weight of theindividual and the differences between the corresponding facialfeatures, facial dimensions, and/or facial locations on the face of theindividual represented in the initial visit data and subsequent visitdata; and predicting, with the one or more processors, based on therelationship, a degree of weight gain and/or loss by the individual thatwill cause the differences between the corresponding facial features,facial dimensions, and/or facial locations on the face of the individualrepresented in the initial visit data and subsequent visit data tobreach the one or more pre-defined ADs.
 5. The method of claim 1,further comprising: categorizing, with the one or more processors, theface of the individual into a NIOSH Headform Category based on theinitial visit data, the subsequent visit data, and/or the differencesbetween the corresponding facial features, facial dimensions, and/orfacial locations on the face of the individual represented in theinitial visit data and subsequent visit data; and determining, with theone or more processors, the one or more pre-defined ADs based on thecategorized NIOSH Headform Category.
 6. The method of claim 5, whereinthe one or more processors are configured such that NIOSH HeadformCategories include small, medium, large, long/narrow, and short/wide. 7.The method of claim 1, further comprising determining, with the one ormore processors, a recommended respirator mask manufacturer and/or modelfor the individual based on the initial visit data, the subsequent visitdata, and/or the differences between the corresponding facial features,facial dimensions, and/or facial locations on the face of the individualrepresented in the initial visit data and subsequent visit data.
 8. Themethod of claim 1, further comprising obtaining, with the one or moreprocessors, demographic information for the individual at the initialrespirator mask fitting visit and/or the subsequent respirator maskfitting visit, the demographic information comprising one or more ofgeographical information about a location of the individual, racialinformation about the individual, information about a gender of theindividual, information about an industry where the individual works, orpublic health information related to the industry where the individualworks.
 9. The method of claim 8, further comprising, determining, withthe one or more processors, a relationship between the demographicinformation of the individual and the differences between thecorresponding facial features, facial dimensions, and/or faciallocations on the face of the individual represented in the initial visitdata and subsequent visit data; and predicting, with the one or moreprocessors, based on the relationship, whether the differences betweenthe corresponding facial features, facial dimensions, and/or faciallocations on the face of the individual represented in future visit datawill breach the one or more pre-defined ADs.
 10. The method of claim 1,further comprising determining, with the one or more processors, basedon the differences between the corresponding facial features, facialdimensions, and/or facial locations on the face of the individualrepresented in the initial visit data and subsequent visit data,presence of a temporary facial blemish; and adjusting, with the one ormore processors, based on the determination of the presence of a facialblemish, the determination of whether the differences between thecorresponding facial features, facial dimensions, and/or faciallocations on the face of the individual represented in the initial visitdata and subsequent visit data breach the one or more pre-defined ADs.11. The method of claim 1, further comprising determining, with the oneor more processors, based on the differences between the correspondingfacial features, facial dimensions, and/or facial locations on the faceof the individual represented in the initial visit data and subsequentvisit data, presence of skin cancer on the face of the individual. 12.The method of claim 1, further comprising determining, with the one ormore processors, based on the differences between the correspondingfacial features, facial dimensions, and/or facial locations on the faceof the individual represented in the initial visit data and subsequentvisit data, presence of heart disease in the individual.
 13. The methodof claim 1, further comprising determining, with the one or moreprocessors, based on the differences between the corresponding facialfeatures, facial dimensions, and/or facial locations on the face of theindividual represented in the initial visit data and subsequent visitdata, presence of asymmetric skin migration indicative of a stroke orBell's Palsy in the individual.
 14. The method of claim 1, furthercomprising determining, with the one or more processors, a recommendedrespirator mask manufacturer and/or model for a different individualbased on the initial visit data, the subsequent visit data, and/or thedifferences between the corresponding facial features, facialdimensions, and/or facial locations on the face of the individualrepresented in the initial visit data and subsequent visit data.
 15. Themethod of claim 14, further comprising obtaining, with the one or moreprocessors, weight information for the individual at the initialrespirator mask fitting visit and the subsequent respirator mask fittingvisit; determining, with the one or more processors, a relationshipbetween a weight of the individual and the differences between thecorresponding facial features, facial dimensions, and/or faciallocations on the face of the individual represented in the initial visitdata and subsequent visit data; obtaining, with the one or moreprocessors, demographic information for the individual at the initialrespirator mask fitting visit and/or the subsequent respirator maskfitting visit, the demographic information comprising one or more ofgeographical information about a location of the individual, racialinformation about the individual, information about a gender of theindividual, information about an industry where the individual works, orpublic health information related to the industry where the individualworks; determining, with the one or more processors, a relationshipbetween the demographic information of the individual and thedifferences between the corresponding facial features, facialdimensions, and/or facial locations on the face of the individualrepresented in the initial visit data and subsequent visit data; anddetermining, with the one or more processors, the recommended respiratormask manufacturer and/or model for the different individual based on (1)the initial visit data, the subsequent visit data, and/or thedifferences between the corresponding facial features, facialdimensions, and/or facial locations on the face of the individualrepresented in the initial visit data and subsequent visit data; (2) therelationship between a weight of the individual and the differencesbetween the corresponding facial features, facial dimensions, and/orfacial locations on the face of the individual represented in theinitial visit data and subsequent visit data; and (3) the relationshipbetween the demographic information of the individual and thedifferences between the corresponding facial features, facialdimensions, and/or facial locations on the face of the individualrepresented in the initial visit data and subsequent visit data.
 16. Themethod of claim 1, wherein determining, with the one or more processors,based on the initial visit data and subsequent visit data, whetherdifferences between corresponding facial features, facial dimensions,and/or facial locations on the face of the individual represented in theinitial visit data and subsequent visit data breach one or morepre-defined ADs comprises comparing a plurality of facial features,facial dimensions, and/or facial locations on the face of the individualrepresented in the initial visit data and subsequent visit data tocorresponding ADs for individual facial features, facial dimensions,and/or facial locations.
 17. The method of claim 16, wherein the one ormore processors are configured such that determining whether differencesbetween corresponding facial features, facial dimensions, and/or faciallocations on the face of the individual represented in the initial visitdata and subsequent visit data breach one or more of the pre-defined ADscomprises determining a weighted combination of the comparisons of theplurality of facial features, facial dimensions, and/or facial locationson the face of the individual represented in the initial visit data andsubsequent visit data to the corresponding ADs for the individual facialfeatures, facial dimensions, and/or facial locations.
 18. The method ofclaim 1, wherein the one or more processors are configured such that theinitial visit data and subsequent visit data each comprise millions ofindividual data points, and determining whether differences betweencorresponding facial features, facial dimensions, and/or faciallocations on the face of the individual represented in the initial visitdata and subsequent visit data breach the one or more pre-defined ADscomprises comparing individual data points in the initial visit data tocorresponding individual data points in the subsequent visit data. 19.The method of claim 1, wherein the one or more processors are configuredsuch that determining whether differences between corresponding facialfeatures, facial dimensions, and/or facial locations on the face of theindividual represented in the initial visit data and subsequent visitdata breach one or more of the pre-defined ADs comprises determining atleast one initial facial volume and at least one subsequent facialvolume of the face of the individual represented in the initial visitdata and subsequent visit data and comparing a difference between the atleast one subsequent facial volume and the at least one initial facialvolume to a corresponding AD for facial volume.
 20. The method of claim1, wherein the one or more processors are configured such thatdetermining whether differences between corresponding facial features,facial dimensions, and/or facial locations on the face of the individualrepresented in the initial visit data and subsequent visit data breachone or more of the pre-defined ADs comprises determining at least oneinitial facial area and at least one subsequent facial area of the faceof the individual represented in the initial visit data and subsequentvisit data and comparing a difference between the at least onesubsequent facial area and the at least one initial facial area to acorresponding AD for facial area.
 21. The method of claim 1, wherein theone or more processors are configured such that determining whetherdifferences between corresponding facial features, facial dimensions,and/or facial locations on the face of the individual represented in theinitial visit data and subsequent visit data breach one or more of thepre-defined ADs comprises determining at least one initial facial pointto point distance and at least one subsequent facial point to pointdistance of the face of the individual represented in the initial visitdata and subsequent visit data and comparing a difference between the atleast one subsequent facial point to point distance and the at least oneinitial facial point to point distance to a corresponding AD for facialpoint to point distance.
 22. The method of claim 1, further comprisingdetermining the one or more pre-defined ADs by: obtaining, with the oneor more processors, at least one first fit test two-dimensional (2D) orthree-dimensional (3D) facial image of a plurality of human or humanmodel test subjects in a statistically significant sample size of humanor human model test subjects; obtaining, with the one or moreprocessors, at least one second fit test two-dimensional (2D) orthree-dimensional (3D) facial image of the plurality of human or humanmodel test subjects in the statistically significant sample size ofhuman or human model test subjects, wherein faces of the plurality ofhuman or human model test subjects are changed between the first fittest and the second fit test; converting, with the one or moreprocessors, the first and second fit test facial images of the pluralityof human or human model test subjects to numerical first and second fittest data for analysis, the first and second fit test datarepresentative of facial features, facial dimensions, and/or faciallocations on the faces of the plurality of human or human model testsubjects; and for those human or human model test subjects in theplurality of human or human model test subjects who experience a changeevent between the first and second fit test, aggregating, with the oneor more processors, the first and second fit test data to determine theone or more pre-defined ADs for the facial features, facial dimensions,and/or facial locations on the faces of the plurality of human or humanmodel test subjects.
 23. The method of claim 22, wherein a change eventcomprises an even after which a human or human model test subject can nolonger be successfully fit tested at the second fit test to a respiratormask used in the first fit test using conventional fit test methods. 24.The method of claim 22 wherein aggregating the first and second fit testdata to determine the one or more pre-defined ADs comprises determiningaverages and standard deviations of differences in measurementsrepresented by the numerical first and second fit test datacorresponding to the facial features, facial dimensions, and/or faciallocations on the faces of the plurality of human or human model testsubjects, and determining the one or more pre-defined ADs based on theaverages and standard deviations of the differences.
 25. The method ofclaim 22, further comprising: validating the one or more pre-defined ADswith fit test data for a plurality of actual respirator users (RU) whoexperience a change event between fit tests.
 26. The method of claim 1,wherein generating, with the one or more processors, the mask fit passindication responsive to differences between the corresponding facialfeatures, facial dimensions, and/or facial locations on the face of theindividual represented in the initial visit data and subsequent visitdata not breaching the one or more pre-defined ADs; or generating, withthe one or more processors, the mask fit fail indication responsive todifferences between the corresponding facial features, facialdimensions, and/or facial locations on the face of the individualrepresented in the initial visit data and subsequent visit databreaching the one or more pre-defined ADs is performed for two or moredifferent types of respirator masks using the same initial visit dataand subsequent visit data.
 27. The method of claim 1, further comprisingperforming visit-over-visit fit testing for (1) personal protectiveequipment (PPE) mandated to require such fit testing, or (2) PPE forwhich a manufacturer recommends such fit testing.
 28. A tangible,non-transitory, machine-readable medium storing instructions that whenexecuted effectuate operations including: obtaining at least one initialthree-dimensional (3D) facial image of an individual from an initialrespirator mask fitting visit; obtaining at least one current 3D facialimage of the individual from a subsequent respirator mask fitting visit;converting the initial facial image and the current facial image tonumerical initial visit data and subsequent visit data for analysis, theinitial visit data and the subsequent visit data representative offacial features, facial dimensions, and/or facial locations on the faceof the individual; identifying facial reference points in the initialvisit data and the subsequent visit data; determining whether the facialreference points in the initial visit data and the subsequent visit datameet alignment criteria; and responsive to a determination that thefacial reference points in the initial visit data and the subsequentvisit data meet the alignment criteria: determining, based on theinitial visit data and subsequent visit data, whether differencesbetween corresponding facial features, facial dimensions, and/or faciallocations on the face of the individual represented in the initial visitdata and subsequent visit data breach one or more pre-defined allowabledeltas (ADs), a given AD comprising a threshold representative of amaximum amount of change in a corresponding facial feature, dimension,or location that would prevent a respirator mask from fitting the faceof the individual; and generating a mask fit pass indication responsiveto differences between the corresponding facial features, facialdimensions, and/or facial locations on the face of the individualrepresented in the initial visit data and subsequent visit data notbreaching the one or more pre-defined ADs; or generating a mask fit failindication responsive to differences between the corresponding facialfeatures, facial dimensions, and/or facial locations on the face of theindividual represented in the initial visit data and subsequent visitdata breaching the one or more pre-defined ADs.
 29. A system comprisingone or more processors and memory storing instructions that whenexecuted by the processors cause the processors to effectuate operationscomprising: obtaining at least one initial three-dimensional (3D) facialimage of an individual from an initial respirator mask fitting visit;obtaining at least one current 3D facial image of the individual from asubsequent respirator mask fitting visit; converting the initial facialimage and the current facial image to numerical initial visit data andsubsequent visit data for analysis, the initial visit data and thesubsequent visit data representative of facial features, facialdimensions, and/or facial locations on the face of the individual;identifying facial reference points in the initial visit data and thesubsequent visit data; determining whether the facial reference pointsin the initial visit data and the subsequent visit data meet alignmentcriteria; and responsive to a determination that the facial referencepoints in the initial visit data and the subsequent visit data meet thealignment criteria: determining, based on the initial visit data andsubsequent visit data, whether differences between corresponding facialfeatures, facial dimensions, and/or facial locations on the face of theindividual represented in the initial visit data and subsequent visitdata breach one or more pre-defined allowable deltas (ADs), a given ADcomprising a threshold representative of a maximum amount of change in acorresponding facial feature, dimension, or location that would preventa respirator mask from fitting the face of the individual; andgenerating a mask fit pass indication responsive to differences betweenthe corresponding facial features, facial dimensions, and/or faciallocations on the face of the individual represented in the initial visitdata and subsequent visit data not breaching the one or more pre-definedADs; or generating a mask fit fail indication responsive to differencesbetween the corresponding facial features, facial dimensions, and/orfacial locations on the face of the individual represented in theinitial visit data and subsequent visit data breaching the one or morepre-defined ADs.